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Fuzzy Sets and Systems
Fuzzy neural networks: a survey
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Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
An improved fuzzy neural network based on T-S model
Expert Systems with Applications: An International Journal
Information and Software Technology
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Extension Neural Network Based on Immune Algorithm for Fault Diagnosis
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
IEEE Transactions on Fuzzy Systems
Development of quantum-based adaptive neuro-fuzzy networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Engineering Applications of Artificial Intelligence
A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Genetically optimized hybrid fuzzy neural networks based on TSK fuzzy rules and polynomial neurons
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Design of rule-based neurofuzzy networks by means of genetic fuzzy set-based granulation
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
A novel self-organizing fuzzy polynomial neural networks with evolutionary FPNs: design and analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Genetically optimized hybrid fuzzy neural networks in modeling software data
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Structural design of optimized polynomial radial basis function neural networks
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Evolutionary design of gdSOFPNN for modeling and prediction of NOx emission process
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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This paper introduces an identification method for nonlinear models in the form of Fuzzy-Neural Networks (FNN). In this model, we use two forms of the fuzzy inference methods--a simplified and linear fuzzy inference, and exploit a standard Error Back Propagation learning algorithm. The FNN modeling and identification environment realizes parameter identification through a synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a Hard C-Means (HCM) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out by combining both genetic optimization (genetic algorithm, GA) and the improved complex method. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model with sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process and NOx emission process data of gas turbine power plant).