期刊文献+
共找到14篇文章
< 1 >
每页显示 20 50 100
Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks
1
作者 翟建锋 周健义 +2 位作者 赵嘉宁 张雷 洪伟 《Journal of Southeast University(English Edition)》 EI CAS 2008年第1期6-9,共4页
A novel behavioral model using three-layer time-delay feed-forward neural networks (TDFFNN)is adopted to model radio frequency (RF)power amplifiers exhibiting memory nonlinearities. In order to extract the paramet... A novel behavioral model using three-layer time-delay feed-forward neural networks (TDFFNN)is adopted to model radio frequency (RF)power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines(TDLs) and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator(VSG) is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain. 展开更多
关键词 behavioral model power amplifier time-delay feed- forward neural network(TDFFNN)
下载PDF
Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification
2
作者 T.Jayasree S.Emerald Shia 《Sound & Vibration》 EI 2021年第2期141-161,共21页
This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Sp... This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature. 展开更多
关键词 Autism spectrum disorder down syndrome feed forward neural network perturbation measures noise parameters cepstral features
下载PDF
A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks
3
作者 张长江 付梦印 金梅 《Journal of Beijing Institute of Technology》 EI CAS 2003年第2期119-124,共6页
A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluct... A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis. 展开更多
关键词 multi layer feed forward neural networks BP algorithm Newton recursive algorithm
下载PDF
Near-infrared Spectral Detection of the Content of Soybean Fat Acids Based on Genetic Multilayer Feed forward Neural Network 被引量:1
4
作者 CHAIYu-hua PANWei NINGHai-long 《Journal of Northeast Agricultural University(English Edition)》 CAS 2005年第1期74-78,共5页
In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data ... In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding. 展开更多
关键词 near infrared multilayer feed forward neural network genetic algorithms SOYBEAN fat acid
下载PDF
An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation
5
作者 Junaid Rashid Sumera Kanwal +2 位作者 Muhammad Wasif Nisar Jungeun Kim Amir Hussain 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1309-1324,共16页
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results i... In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy. 展开更多
关键词 Software cost estimation neural network backpropagation forward neural networks software effort estimation artificial neural network
下载PDF
Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
6
作者 Mohd Herwan Sulaiman Zuriani Mustaffa 《Energy and AI》 EI 2024年第2期346-362,共17页
This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power genera... This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability. 展开更多
关键词 Deep learning neural networks Evolutionary mating algorithm Feed forward neural networks Metaheuristic Optimizers Solar PV
原文传递
Application of neural network in the study of combustion rate of natural gas/diesel dual fuel engine 被引量:1
7
作者 严兆大 周重光 +2 位作者 苏石川 刘震涛 王希珍 《Journal of Zhejiang University Science》 EI CSCD 2003年第2期170-174,共5页
In order to predict and improve the performance of natural gas/diesel dual fuel engine (DFE), a combustion rate model based on forward neural network was built to study the combustion process of the DFE. The effect ... In order to predict and improve the performance of natural gas/diesel dual fuel engine (DFE), a combustion rate model based on forward neural network was built to study the combustion process of the DFE. The effect of the operatin g parameters on combustion rate was also studied by means of this model. The stu dy showed that the predicted results were good agreement with the experimental d a ta. It was proved that the developed combustion rate model could be used to succ essfully predict and optimize the combustion process of dual fuel engine. 展开更多
关键词 Dual fuel engine forward neural network Rate of combu stion
下载PDF
Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion 被引量:1
8
作者 Tahani S. Gendy Amal S. Zakhary Salwa A. Ghoneim 《World Journal of Engineering and Technology》 2021年第4期816-847,共32页
Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied cat... Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> and Pd/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (<i><span style="white-space:nowrap;"><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">&oslash;</span></span></i>) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners w<span style="white-space:normal;font-family:;" "="">as</span><span style="white-space:normal;font-family:;" "=""> scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (<i>r</i>), the radial distance from the center line of the flame, and (<i>x</i>), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three</span><span style="white-space:normal;font-family:;" "="">-</span><span style="white-space:normal;font-family:;" "="">layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate </span><span style="white-space:normal;font-family:;" "="">the </span><span style="white-space:normal;font-family:;" "="">effects of coded <i>R</i> and <i>X</i> input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of  & F_Ratio are 0.9181</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9809 & 634.5</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 3528.8 for RSM method compared to 0.9857</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9951 & 7636.4</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 24</span><span style="white-space:normal;font-family:;" "="">,</span><span style="white-space:normal;font-family:;" "="">028.4 for ANN method beside lower values </span><span style="white-space:normal;font-family:;" "="">for error analysis terms.</span> 展开更多
关键词 Catalytic Combustion Fuel Lean/Fuel Rich Noble Metals Burners Thermal structure MODELING Artificial neural Network Response Surface Methodology Feed forward neural Network
下载PDF
Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms 被引量:1
9
作者 Aqsa Aqdus Rashid Amin +3 位作者 Sadia Ramzan Sultan S.Alshamrani Abdullah Alshehri El-Sayed M.El-kenawy 《Computers, Materials & Continua》 SCIE EI 2023年第1期1413-1435,共23页
The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data.The traffic control and data forwarding functions are decoup... The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data.The traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be programmable.Each switch in SDN keeps track of forwarding information in a flow table.The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets.Due to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding capabilities.So,the SDN must handle traffic from across the whole network.The flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy consumption.Whenever the flow table is abused and overflowing,the usual regulations cannot be executed quickly.In this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low rate.This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)controller.The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and throughput.The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network.By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations,the evaluation tool examines every flow against a set of criteria.The FFNN with DT and K-means algorithms obtain accuracies of 96.29%and 97.51%,respectively,in the identification of collision flows,according to the experimental outcome when associated with existing methods from the literature. 展开更多
关键词 5G networks software-defined networking(SDN) OpenFlow load balancing machine learning(ML) feed forward neural network(FFNN) k-means and decision tree(DT)
下载PDF
Rock burst prediction based on genetic algorithms and extreme learning machine 被引量:21
10
作者 李天正 李永鑫 杨小礼 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第9期2105-2113,共9页
Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic... Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering. 展开更多
关键词 extreme learning machine feed forward neural network rock burst prediction rock excavation
下载PDF
Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine LeAnalysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learningarning
11
作者 Muhammad Adnan Khan Mubashar Habib +4 位作者 Shazia Saqib Tahir Alyas Khalid Masood Khan Mohammed A.Al Ghamdi Sultan H.Almotiri 《Computers, Materials & Continua》 SCIE EI 2021年第1期691-706,共16页
The innovation and development in data science have an impact in all trades of life.The commercialization of sport has encouraged players,coaches,and other concerns to use technology to be in better position than r th... The innovation and development in data science have an impact in all trades of life.The commercialization of sport has encouraged players,coaches,and other concerns to use technology to be in better position than r their opponents.In the past,the focus was on improved training techniques for better physical performance.These days,sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential players.Sports analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the past.The impact of a smart player on the success of a team is always a big question mark before the start of a match.The fans always want to know performance analysis of these superstar players and they always are interested to get to know more about their favorite player and they always have high hopes from their favorite player.Machine learning(ML)based techniques help in predicting the performance of an individual player as well as for the whole team.The statistics are very vital and useful for management,fans,and expert analysis.In our proposed framework,the adaptive back propagation neural network(ABPNN)model is used for the prediction of a player’s performance.The data is collected from football websites,and the results are stored in the cloud for fast fetching of data.They can be retrieved anywhere in the world through cloud storage.The results are computed with 94%accuracy and the performance of the smart player is formulated for the success of a team. 展开更多
关键词 Machine learning adaptive feed forwarded neural network adaptive back propagation neural network cloud computing fetching
下载PDF
An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems 被引量:1
12
作者 Saeid Raziani Sajad Ahmadian +1 位作者 Seyed Mohammad Jafar Jalali Abdolah Chalechale 《Journal of Bionic Engineering》 SCIE EI CSCD 2022年第5期1504-1521,共18页
Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been dev... Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets. 展开更多
关键词 Feed forward neural network Meta-heuristic algorithm Whale optimization algorithm Optimization CLASSIFICATION Bionic algorithm
原文传递
STUDYING THE ABRASION BEHAVIOR OF RUBBERY MATERIALS WITH COMBINED DESIGN OF EXPERIMENT-ARTIFICIAL NEURAL NETWORK 被引量:1
13
作者 Mehdi Shiva Hossein Atashi Mahtab Hassanpourfard 《Chinese Journal of Polymer Science》 SCIE CAS CSCD 2012年第4期520-529,共10页
In this study, an application of artificial neural network (ANN) has been presented in modeling and studying the effect of compounding variables on abrasion behavior of rubber formulations. Three case studies were c... In this study, an application of artificial neural network (ANN) has been presented in modeling and studying the effect of compounding variables on abrasion behavior of rubber formulations. Three case studies were carried out in which the experiment data were collected according to classical response surface designs. Besides developing the ANN models, we developed response surface methodology (RSM) to confirm the ANN predictions. A simple relation was employed for determination of relative importance of each variable according to ANN models. It was shown through these case studies that ANN models delivered very good data fitting and their simulating curves could help the researchers to better understand the abrasion behavior. 展开更多
关键词 ABRASION Feed forward neural networks Rubber compounding Central composite design.
原文传递
Artificial intelligence model for predicting geomechanical characteristics using easy-to-acquire offset logs without deploying logging tools
14
作者 Temitope F.Ogunkunle Emmanuel E.Okoro +2 位作者 Oluwatosin J.Rotimi Paul Igbinedion David I.Olatunji 《Petroleum》 EI CSCD 2022年第2期192-203,共12页
This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock p... This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock properties at offset locations.The Random Forest algorithm was used for direct prediction of the sonic data without considering the depth range of the facies;while Feed forward Neural network was used to predict the sonic data with emphasis on the lithofacies depths.The accuracy of these approaches was used in choosing the best and the most robust model for predicting sonic data when estimating formation strength and mechnical properties.Acoustic log was predicted after training a combination of caliper log,gamma log,depth,density log and resistivity log from offset wells.5 hidden layers that accounts for the data structural complexities was included in the model architecture.A multilayer perceptron network was adopted for the Random forest algorithm to handle linear combinations of input data set.Diverse error computations were used to evaluate the performance of the model.Lastly,mechanical properties and sanding potential was evaluated using standard relations and appropriate depositional conditions.Random forest algorithm gave the best prediction accuracy of more than 96%,but the Feed forward network has the lower mean absolute error and mean squared error of 2.75 and 5.93 respectively.Generally,the predicted compressive and shear wave velocity show increase of values with depth,a behavior that is capable of identifying payzone characteristics.This was validated by the distinction seen within the 200 feet gas sand formation in the deeper portion of the studied well(9600e9800 feet).Potential failure portions of the wells,a common feature in the field,were inferred from the sanding potential computed using the predicted mechanical properties value. 展开更多
关键词 Shear wave velocity Mechanical properties Random forest Feed forward neural network Sanding potential
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部