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Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks 被引量:4
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作者 Yu Liu Jing-Jun Zhu +5 位作者 Neil Roberts Ke-Ming Chen Yu-Lu Yan Shuang-Rong Mo Peng Gu Hao-Yang Xing 《Nuclear Science and Techniques》 SCIE CAS CSCD 2019年第10期30-39,共10页
Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in hi... Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics. 展开更多
关键词 Saturated signals artificial neural networks(ANNs) RECOVERY of signal waveform Generalized radial basis function Backpropagation neural network ELMAN neural network
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Calculation method of ship collision force on bridge using artificial neural network 被引量:4
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作者 Wei FAN Wan-cheng YUAN Qi-wu FAN 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2008年第5期614-623,共10页
Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent st... Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software. 展开更多
关键词 Ship-bridge collision force Finite element method (FEM) artificial neural network (ANN) Radial basis function neural network (RBFNN)
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A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak 被引量:9
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作者 A.Sayadi M.Monjezi +1 位作者 N.Talebi Manoj Khandelwal 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2013年第4期318-324,共7页
In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and... In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome.Since many parameters affect the blasting results in a complicated mechanism,employment of robust methods such as artificial neural network may be very useful.In this regard,this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran.Back propagation neural network(BPNN) and radial basis function neural network(RBFNN) are adopted for the simulation.Also,regression analysis is performed between independent and dependent variables.For the BPNN modeling,a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN,architecture 636-2 with spread factor of 0.79 provides maximum prediction aptitude.Performance comparison of the developed models is fulfilled using value account for(VAF),root mean square error(RMSE),determination coefficient(R2) and maximum relative error(MRE).As such,it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error.Also,sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak,respectively.On the other hand,for both of the outputs,specific charge is the least effective parameter. 展开更多
关键词 Rock fragmentation Backbreak artificial neural network Back propagation Radial basis function
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Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City,China 被引量:2
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作者 QIAO Weifeng GAO Junbo +3 位作者 LIU Yansui QIN Yueheng LU Cheng JI Qingqing 《Chinese Geographical Science》 SCIE CSCD 2017年第5期735-746,共12页
In this paper, the artificial neural network(ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the compr... In this paper, the artificial neural network(ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas. 展开更多
关键词 urban land intensive use functional area artificial neural network (ANN) model Nanjing City
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Application of FCM Algorithm Combined with Artificial Neural Network in TBM Operation Data
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作者 Jingyi Fang Xueguan Song +1 位作者 Nianmin Yao Maolin Shi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第1期397-417,共21页
Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine.However,the traditional fuzzy clustering algorithm based on objective function is difficult to effectively cluster functional da... Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine.However,the traditional fuzzy clustering algorithm based on objective function is difficult to effectively cluster functional data.We propose a new Fuzzy clustering algorithm,namely FCM-ANN algorithm.The algorithm replaces the clustering prototype of the FCM algorithm with the predicted value of the artificial neural network.This makes the algorithm not only satisfy the clustering based on the traditional similarity criterion,but also can effectively cluster the functional data.In this paper,we first use the t-test as an evaluation index and apply the FCM-ANN algorithm to the synthetic datasets for validity testing.Then the algorithm is applied to TBM operation data and combined with the crossvalidation method to predict the tunneling speed.The predicted results are evaluated by RMSE and R^(2).According to the experimental results on the synthetic datasets,we obtain the relationship among the membership threshold,the number of samples,the number of attributes and the noise.Accordingly,the datasets can be effectively adjusted.Applying the FCM-ANN algorithm to the TBM operation data can accurately predict the tunneling speed.The FCM-ANN algorithm has improved the traditional fuzzy clustering algorithm,which can be used not only for the prediction of tunneling speed of TBM but also for clustering or prediction of other functional data. 展开更多
关键词 Data clustering FCM artificial neural network functional data TBM
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Signal prediction based on empirical mode decomposition and artificial neural networks
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作者 Wang Yong Liu Yanping Yang Jing 《Geodesy and Geodynamics》 2012年第1期52-56,共5页
In view of the usefulness of Empirical Mode Decomposition (EMD), Artificial Neural Networks ( ANN), and Most Relevant Matching Extension (MRME) methods in dealing with nonlinear signals, we pro- pose a new way o... In view of the usefulness of Empirical Mode Decomposition (EMD), Artificial Neural Networks ( ANN), and Most Relevant Matching Extension (MRME) methods in dealing with nonlinear signals, we pro- pose a new way of combining these methods to deal with signal prediction. We found the results of combining EMD with either ANN or MRME to have higher prediction precision for a time series than the result of using EMD alone. 展开更多
关键词 EMD (Empirical Mode Decomposition) ANN artificial neural networks) MRME (Most Relevant Matching Extension) IMF (Intrinsic Mode function endpoint problem RBF (Radial Basis function
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Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three-Dimensional Hydrodynamic Models
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作者 Weibo Chen Wencheng Liu +1 位作者 Weiche Huang Hongming Liu 《Computational Water, Energy, and Environmental Engineering》 2017年第1期107-128,共22页
The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series ... The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series at boundaries, river geometry, and adjusted coefficients. Therefore, an artificial neural network (ANN) technique using a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is adopted as an effective alternative in salinity simulation studies. The present study focuses on comparing the performance of BPNN, RBFNN, and three-dimensional hydrodynamic models as applied to a tidal estuarine system. The observed salinity data sets collected from 18 to 22 May, 16 to 22 October, and 26 to 30 October 2002 (totaling 4320 data points) were used for BPNN and RBFNN model training and for hydrodynamic model calibration. The data sets collected from 30 May to 2 June and 11 to 15 November 2002 (totaling 2592 data points) were adopted for BPNN and RBFNN model verification and for hydrodynamic model verification. The results revealed that the ANN (BPNN and RBFNN) models were capable of predicting the nonlinear time series behavior of salinity to the multiple forcing signals of water stages at different stations and freshwater input at upstream boundaries. The salinity predicted by the ANN models was better than that predicted by the physically based hydrodynamic model. This study suggests that BPNN and RBFNN models are easy-to-use modeling tools for simulating the salinity variation in a tidal estuarine system. 展开更多
关键词 SALINITY Variation artificial neural network Backpropagation Algorithm Radial Basis function neural network THREE-DIMENSIONAL Hydrodynamic Model TIDAL ESTUARY
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Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks
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作者 Cisse Sory Ibrahima Jianwu Xue Thierno Gueye 《Journal of Management Science & Engineering Research》 2021年第2期33-39,共7页
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp... Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast. 展开更多
关键词 Inventory management Demand forecasting Seasonal time series artificial neural networks Transfer function Inventory management Demand forecasting Seasonal time series artificial neural networks Transfer function
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Which return regime induces overconfidence behavior?Artificial intelligence and a nonlinear approach
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作者 Esra Alp Coşkun Hakan Kahyaoglu Chi Keung Marco Lau 《Financial Innovation》 2023年第1期1135-1168,共34页
Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as over... Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as overtrading following positive returns,may lead to inefficiencies in stock markets.To the best of our knowledge,this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude.We examine whether investors in an emerging stock market(Borsa Istanbul)exhibit overconfidence behavior using a feed-forward,neural network,nonlinear Granger causality test and nonlinear impulseresponse functions based on local projections.These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional,multivariate time series.The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature,which is the key contribution of the study.The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon.Overconfidence is more persistent in the low-than in the high-return regime.In the negative interest-rate period,a high-return regime induces overconfidence behavior,whereas in the positive interest-rate period,a low-return regime induces overconfidence behavior.Based on the empirical findings,investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies,particularly in low-return regimes. 展开更多
关键词 OVERCONFIDENCE Nonlinear Granger causality artificial intelligence Feedforward neural networks Nonlinear impulse-response functions Local projections Return regime
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Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network 被引量:2
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作者 YANG Xiao-hua HUANG Jing-feng +2 位作者 WANG Jian-wen WANG Xiu-zhen LIU Zhan-yu 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第6期883-895,共13页
Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices ... Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs. 展开更多
关键词 artificial neural network (ANN) Radial basis function (RBF) Remote sensing RICE Vegetation index (VI)
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Development of Trees Management System Using Radial Basis Function Neural Network for Rain Forecast 被引量:1
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作者 Hasnul Auzani Khairusy Syakirin Has-Yun Farah Aniza Mohd Nazri 《Computational Water, Energy, and Environmental Engineering》 2022年第1期1-10,共10页
Agriculture and farming are mainly dependent on weather especially in Malaysia as it received heavy rainfall throughout the years. An efficient crop or tree management system with a weather forecast needed for suitabl... Agriculture and farming are mainly dependent on weather especially in Malaysia as it received heavy rainfall throughout the years. An efficient crop or tree management system with a weather forecast needed for suitable planning of farming operation. Radial Basis Function Neural Network (RBFNN) algorithm was used in this study to predict rainfall and the main focus of this study is to analyze the factor that affect</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the performance of neural model. This study found that the model works better the more hidden nodes and the optimum learning rate is 0.01 with the RMSE 49% and the percentage accuracy is 57%. Besides that, it is found that the meteorology data also affect the model performance. Future research can be conducted to improve the rainfall forecast of this study and improv</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">e</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the tree management system. 展开更多
关键词 Tree Management Radial Basis function Rain Prediction artificial neural network
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Feature Selection Based on Adaptive Fuzzy Membership Functions 被引量:1
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作者 XIE Yan-Tao SANG Nong ZHANG Tian-Xu 《自动化学报》 EI CSCD 北大核心 2006年第4期496-503,共8页
Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the ... Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm. 展开更多
关键词 Membership function feature selection architecture pruning artificial neural networks
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Simulation and prediction of monthly accumulated runoff,based on several neural network models under poor data availability 被引量:1
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作者 JianPing Qian JianPing Zhao +2 位作者 Yi Liu XinLong Feng DongWei Gui 《Research in Cold and Arid Regions》 CSCD 2018年第6期468-481,共14页
Most previous research on areas with abundant rainfall shows that simulations using rainfall-runoff modes have a very high prediction accuracy and applicability when using a back-propagation(BP), feed-forward, multila... Most previous research on areas with abundant rainfall shows that simulations using rainfall-runoff modes have a very high prediction accuracy and applicability when using a back-propagation(BP), feed-forward, multilayer perceptron artificial neural network(ANN). However, in runoff areas with relatively low rainfall or a dry climate, more studies are needed. In these areas—of which oasis-plain areas are a particularly good example—the existence and development of runoff depends largely on that which is generated from alpine regions. Quantitative analysis of the uncertainty of runoff simulation under climate change is the key to improving the utilization and management of water resources in arid areas. Therefore, in this context, three kinds of BP feed-forward, three-layer ANNs with similar structure were chosen as models in this paper.Taking the oasis–plain region traverse by the Qira River Basin in Xinjiang, China, as the research area, the monthly accumulated runoff of the Qira River in the next month was simulated and predicted. The results showed that the training precision of a compact wavelet neural network is low; but from the forecasting results, it could be concluded that the training algorithm can better reflect the whole law of samples. The traditional artificial neural network(TANN) model and radial basis-function neural network(RBFNN) model showed higher accuracy in the training and prediction stage. However, the TANN model, more sensitive to the selection of input variables, requires a large number of numerical simulations to determine the appropriate input variables and the number of hidden-layer neurons. Hence, The RBFNN model is more suitable for the study of such problems. And it can be extended to other similar research arid-oasis areas on the southern edge of the Kunlun Mountains and provides a reference for sustainable water-resource management of arid-oasis areas. 展开更多
关键词 OASIS artificial neural network radial basis function wavelet function runoff simulation
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A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework 被引量:1
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作者 Lekan Taofeek Popoola Gutti Babagana Alfred Akpoveta Susu 《Advances in Chemical Engineering and Science》 2013年第2期164-170,共7页
This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (... This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method. 展开更多
关键词 artificial neural network CRUDE Oil Distillation Column Genetic ALGORITHM FRAMEWORK Sigmoidal Transfer function BACK-PROPAGATION ALGORITHM
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Structural Reliability Analysis for Implicit Performance with Legendre Orthogonal Neural Network Method
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作者 Lirong Sha Tongyu Wang 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2016年第1期60-66,共7页
In order to evaluate the failure probability of a complicated structure,the structural responses usually need to be estimated by some numerical analysis methods such as finite element method( FEM). The response surfac... In order to evaluate the failure probability of a complicated structure,the structural responses usually need to be estimated by some numerical analysis methods such as finite element method( FEM). The response surface method( RSM) can be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However,the conventional RSM is time-consuming or cumbersome if the number of random variables is large. This paper proposes a Legendre orthogonal neural network( LONN)-based RSM to estimate the structural reliability. In this method,the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability analysis method,i.e. first-order reliability methods( FORM) to calculate the failure probability of the structure.Numerical examples show that the proposed approach is applicable to structural reliability analysis,as well as the structure with implicit performance functions. 展开更多
关键词 RELIABILITY orthogonal function performance function artificial neural network
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Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19
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作者 Zulqurnain Sabir Abeer S.Alnahdi +4 位作者 Mdi Begum Jeelani Mohamed A.Abdelkawy Muhammad Asif Zahoor Raja Dumitru Baleanu Muhammad Mubashar Hussain 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第5期763-785,共23页
The present investigations are associated with designing Morlet wavelet neural network(MWNN)for solving a class of susceptible,infected,treatment and recovered(SITR)fractal systems of COVID-19 propagation and control.... The present investigations are associated with designing Morlet wavelet neural network(MWNN)for solving a class of susceptible,infected,treatment and recovered(SITR)fractal systems of COVID-19 propagation and control.The structure of an error function is accessible using the SITR differential form and its initial conditions.The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm(GA)and active-set algorithm(ASA),i.e.,MWNN-GA-ASA.The detail of each class of the SITR nonlinear COVID-19 system is also discussed.The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method.The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method.The plots of the absolute error,convergence analysis,histogram,performancemeasures,and boxplots are also provided to find the exactness,dependability and stability of the MWNN-GA-ASA. 展开更多
关键词 Nonlinear SITR model morlet function artificial neural networks RUNGE-KUTTA TREATMENT genetic algorithm TREATMENT active-set
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Sensitivity Analysis of Radial Basis Function Networks for River Stage Forecasting
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作者 Christian Walker Dawson 《Journal of Software Engineering and Applications》 2020年第12期327-347,共21页
<div style="text-align:justify;"> <span style="font-family:Verdana;">Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addr... <div style="text-align:justify;"> <span style="font-family:Verdana;">Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.</span> </div> 展开更多
关键词 artificial neural networks Backward Chaining Multi-Layer Perceptron Partial Derivative Radial Basis function Sensitivity Analysis River Stage Forecasting
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An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores
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作者 Chibuzo Cosmas Nwanwe Ugochukwu Ilozurike Duru +1 位作者 Charley Anyadiegwu Azunna I.B.Ekejuba 《Petroleum Research》 EI 2023年第3期370-385,共16页
Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic mo... Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model. 展开更多
关键词 Flowing bottom-hole pressure Real-time prediction artificial neural network Visible mathematical model Levenberg-marquardt optimization ALGORITHM Hyperbolic tangent activation function Empirical correlations Mechanistic models
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基于人工智能技术的机器人运动控制系统设计
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作者 李艳红 《现代电子技术》 北大核心 2024年第10期117-122,共6页
设计一种基于人工智能技术的机器人运动控制系统,确保机器人更好地理解人类的意图,并提供更加人性化的服务。该系统通过运动数据采集与传输组件连接机器人的轴电机,采集机器人当前运动数据后,将其传输到控制器组件内,控制器组件依托X86... 设计一种基于人工智能技术的机器人运动控制系统,确保机器人更好地理解人类的意图,并提供更加人性化的服务。该系统通过运动数据采集与传输组件连接机器人的轴电机,采集机器人当前运动数据后,将其传输到控制器组件内,控制器组件依托X86架构工控机,使用PIC总线将采集到的机器人当前运动数据发送到基于人工智能技术的机器人运动路径规划模块内。该模块运用人工智能技术中的A*算法获取机器人轨迹路径规划结果后,依据该路径规划结果,将人工智能技术中的神经网络和模糊B样条基函数相结合,建立模糊B样条基函数神经网络控制器。该控制器输出机器人运动控制指令,并发送给伺服驱动器组件,伺服驱动器负责驱动机器人轴电机,控制机器人运动。实验结果表明:所设计系统具备较强的机器人路径规划能力,可在复杂路径情况下实现机器人运动控制,且控制精度和控制阶跃响应能力均较强。 展开更多
关键词 人工智能 机器人 运动控制系统 模糊B样条基函数 神经网络 路径规划
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Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature 被引量:2
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作者 Samuel Admasie Syed Basit Ali Bukhari +2 位作者 Teke Gush Raza Haider Chul Hwan Kim 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2020年第3期511-520,共10页
The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants,... The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal. 展开更多
关键词 Distributed energy resource(DER) intrinsic mode function(IMF) grey wolf optimized artificial neural network(GWO-ANN) intelligent islanding detection method(IIDM) MICROGRID
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