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Prediction of dissolved oxygen content changes based on two-dimensional behavior features of fish school and T-S fuzzy neural network
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作者 Yu-jun Bao Chang-ying Ji Bing Zhang 《Water Science and Engineering》 EI CAS CSCD 2022年第3期210-217,共8页
Dissolved oxygen(DO)content is an important index of river water quality.Water quality sensors have been used in China for urban river water monitoring and DO content prediction.However,water quality sensors are expen... Dissolved oxygen(DO)content is an important index of river water quality.Water quality sensors have been used in China for urban river water monitoring and DO content prediction.However,water quality sensors are expensive and difficult to maintain,and have a short operation period and difficult to maintain.This study developed a scientific and accurate method for prediction of DO content changes using fish school features.The behavioral features of the Carassius auratus fish school were described using two-dimensional fish school images.The degree of DO content decline was graded into five levels,and the corresponding numerical ranges of cluster characteristic parameters were determined by considering the opinions of ichthyologists.Finally,the variation of DO content was predicted using the characteristic parameters of the fish school and the multiple-input single-output Takagi-Sugeno fuzzy neural network.The prediction results were basically consistent with the actual variations of DO content.Therefore,it is feasible to use the behavioral features of the fish school to dynamically predict the level of DO content in water,and this method is especially suitable for prediction of sharp decline of DO content in a relatively short time. 展开更多
关键词 Fish behavior Cluster feature DO fuzzy neural network Water quality monitoring
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Prediction of the undrained shear strength of remolded soil with non-linear regression,fuzzy logic,and artificial neural network
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作者 YÜNKÜL Kaan KARAÇOR Fatih +1 位作者 GÜRBÜZ Ayhan BUDAK TahsinÖmür 《Journal of Mountain Science》 SCIE CSCD 2024年第9期3108-3122,共15页
This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results... This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination. 展开更多
关键词 Undrained shear strength Liquidity index Water content ratio Non-linear regression Artificial neural networks fuzzy logic
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Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium
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作者 Anukwonke Maxwell Chukwuma Chibueze Ikechukwu Godwills +1 位作者 Cynthia C. Nwaeju Osakwe Francis Onyemachi 《International Journal of Nonferrous Metallurgy》 2024年第1期1-19,共19页
In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties ... In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R). 展开更多
关键词 Al-5%Mg Alloy NEODYMIUM Artificial neural network fuzzy Logic Average Grain Size and Mechanical Properties
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Intelligent vehicle lateral controller design based on genetic algorithmand T-S fuzzy-neural network
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作者 RuanJiuhong FuMengyin LiYibin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2005年第2期382-387,共6页
Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be reg... Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem. 展开更多
关键词 intelligent vehicle genetic algorithm fuzzy-neural network lateral control robustness.
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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria
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作者 Djeldjli Halima Benatiallah Djelloul +3 位作者 Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra 《Computers, Materials & Continua》 SCIE EI 2024年第6期4725-4740,共16页
When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global s... When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes. 展开更多
关键词 Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation
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Alternative Method of Constructing Granular Neural Networks
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作者 Yushan Yin Witold Pedrycz Zhiwu Li 《Computers, Materials & Continua》 SCIE EI 2024年第4期623-650,共28页
Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The a... Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information granules.Optimizing this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance. 展开更多
关键词 Granular neural network granular connection interval analysis triangular fuzzy numbers particle swarm optimization(PSO)
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A Neuro T-Norm Fuzzy Logic Based System
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作者 Alex Tserkovny 《Journal of Software Engineering and Applications》 2024年第8期638-663,共26页
In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has signifi... In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM. 展开更多
关键词 Neuro-fuzzy System neural network fuzzy Logic Modus Ponnens Modus Tollens fuzzy Conditional Inference
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A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system 被引量:1
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作者 Min Ding Zili Tao +3 位作者 Bo Hu Meng Ye Yingxiong Ou Ryuichi Yokoyama 《Global Energy Interconnection》 EI CSCD 2023年第5期554-566,共13页
When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power refer... When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation. 展开更多
关键词 Maximum wind power tracking fuzzy control neural network
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基于T-S模糊神经网络的光伏发电机组自动控制
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作者 杨振睿 沈主浮 +2 位作者 孙辰 蔡斌 姜宽 《机械与电子》 2024年第2期35-39,共5页
光照情况变化会使光伏发电机组功率呈现不稳定性,加大光伏发电机组控制难度,为此,设计了基于T-S模糊神经网络的光伏发电机组自动控制方法。构建光伏阵列数学模型,分析在均匀和不均匀2种光照情况下光伏发电机组特性曲线。以分析结果为依... 光照情况变化会使光伏发电机组功率呈现不稳定性,加大光伏发电机组控制难度,为此,设计了基于T-S模糊神经网络的光伏发电机组自动控制方法。构建光伏阵列数学模型,分析在均匀和不均匀2种光照情况下光伏发电机组特性曲线。以分析结果为依据,采用T-S模糊神经网络构建光伏发电机组自动控制模型。为保证良好的控制效果,引入定比因子优化隶属度函数,输出最佳跟踪结果,结合最佳跟踪结果和自动控制模型实现光伏发电机组自动控制。测试结果显示,该方法能够完成光伏阵列特性分析,控制效果好。 展开更多
关键词 t-s模糊神经网络 光伏发电机组 自动控制 特性曲线 最大功率点 光照情况
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A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network 被引量:6
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作者 金龙 金健 姚才 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2005年第3期428-435,共8页
In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the ... In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model. 展开更多
关键词 modular fuzzy neural network short-term climate prediction flood season
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Fault-Tolerant Control of Nonlinear Systems Based on Fuzzy Neural Networks 被引量:1
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作者 左东升 姜建国 《Journal of Donghua University(English Edition)》 EI CAS 2009年第6期634-638,共5页
Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tole... Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tolerant control method based on fuzzy neural networks was presented for nonlinear systems in this paper. The fault parameters were designed to detect the fault, adaptive updating method was introduced to estimate and track fault, and fuzzy neural networks were used to adjust the fault parameters and construct automated fault diagnosis. And the fault compeusation control force, which was given by fault estimation, was used to realize adaptive fault-tolerant control. This framework leaded to a simple structure, an accurate detection, and a high robusmess. The simulation results in induction motor show that it is still able to work well with high dynamic performance and control precision under the condition of motor parameters' variation fault and load torque disturbance. 展开更多
关键词 fuzzy neural networks nonlinear sYStem fault-tolerant control ADAPTIVE
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Short-term load forecasting based on fuzzy neural network
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作者 DONG Liang MU Zhichun (Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China) 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 1997年第3期46-48,53,共4页
The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e... The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory. 展开更多
关键词 short-term load forecasting fuzzy control fuzzy neural networks
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Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference
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作者 Evans Nyasha Chogumaira Takashi Hiyama 《Energy and Power Engineering》 2011年第1期9-16,共8页
This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-tu... This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes. 展开更多
关键词 ELECTRICITY PRICE Forecasting SHORt-TERM Load Forecasting ELECTRICITY MARKETS Artificial neural networks fuzzy LOGIC
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Prediction Model of Dissolved Oxygen Fuzzy System in Aquaculture Pond Based on Neural Network 被引量:4
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作者 王瑞梅 傅泽田 何有缘 《Agricultural Science & Technology》 CAS 2010年第8期14-18,共5页
A dissolved oxygen fuzzy system predicting model based on neural network was put forward in this study. 106 groups of data were used to confirm the fitness of the predicting model. The first 80 groups of data were act... A dissolved oxygen fuzzy system predicting model based on neural network was put forward in this study. 106 groups of data were used to confirm the fitness of the predicting model. The first 80 groups of data were acted as training input and the other 26 groups of data were acted as the confirmed data in the system. The result showed that the testing data was approximately the same as the predicted data. So it gave a new way to solve the problem that the status of the water quality couldn't be predicted in time and it's hard to watching and measuring the factors dynamic. 展开更多
关键词 Aquaculture pond Dissolved oxygen fuzzy system neural network
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A Short-Term Traffic Flow Prediction ModelBased on Quantum Genetic Algorithm andFuzzy RBF Neural Networks
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作者 Kun Zhang 《计算机科学与技术汇刊(中英文版)》 2016年第1期24-39,共16页
关键词 神经网络 流动模拟 基因算法 RBF 交通 预言 短期 ARIMA
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APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS 被引量:1
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作者 周洁敏 林刚 +1 位作者 宫淑丽 陶云刚 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2001年第1期91-96,共6页
At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-se... At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter. 展开更多
关键词 MULTI-SENSOR data fus ion fuzzy neural network rotating mechanical fault diagnosis grade of members hip
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A REALIZATION OF FUZZY LOGIC BY A NEURAL NETWORK 被引量:1
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作者 杨忠 鲍明 赵淳生 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 1995年第1期104-108,共5页
This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and N... This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model. 展开更多
关键词 fuzzy logic NEURON neural network propagation algorithm fault diagnosis
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INDUCTION MOTOR SPEED CONTROL SYSTEM BASED ON FUZZY NEURAL NETWORK 被引量:1
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作者 徐小增 李叶松 秦忆 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2004年第3期195-199,共5页
A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teachin... A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness. 展开更多
关键词 induction motor fuzzy neural network vector control speed control system
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RBF neural network regression model based on fuzzy observations 被引量:1
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作者 朱红霞 沈炯 苏志刚 《Journal of Southeast University(English Edition)》 EI CAS 2013年第4期400-406,共7页
A fuzzy observations-based radial basis function neural network (FORBFNN) is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership fu... A fuzzy observations-based radial basis function neural network (FORBFNN) is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization ( EM ) algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy. 展开更多
关键词 radial basis function neural network (RBFNN) fuzzy membership function imprecise observation regression model
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AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS 被引量:3
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作者 赵东标 KeshengWang OliverKrimmel 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2000年第2期169-175,共7页
Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificia... Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities. 展开更多
关键词 tool condition monitoring neural networks fuzzy logic acoustic emission force sensor fuzzy neural networks
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