Simplifying neural networks by soft weight-sharing
Neural Computation
Fuzzy information engineering: a guided tour of applications
Fuzzy information engineering: a guided tour of applications
On the combination of fuzzy logic and evolutionary computation: a short review and bibliography
Fuzzy evolutionary computation
An architecture of fuzzy neural networks for linguistic processing
Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Soft Computing and Its Applications
Soft Computing and Its Applications
Fuzzy Neural Network Theory and Application
Fuzzy Neural Network Theory and Application
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Linguistic time series forecasting using fuzzy recurrent neural network
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Dynamic data mining technique for rules extraction in a process of battery charging
Applied Soft Computing
Fuzzy time series prediction method based on fuzzy recurrent neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent fuzzy network for fuzzy temporal sequence processing and gesture recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Time series forecasting with a hybrid clustering scheme and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Recurrent neuro-fuzzy networks for nonlinear process modeling
IEEE Transactions on Neural Networks
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs
IEEE Transactions on Neural Networks
Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series
IEEE Transactions on Neural Networks
On the dynamical modeling with neural fuzzy networks
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks
IEEE Transactions on Neural Networks
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
IEEE Transactions on Neural Networks
Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Fuzzy wavelet neural network models for prediction and identification of dynamical systems
IEEE Transactions on Neural Networks
Recurrent fuzzy system design using elite-guided continuous ant colony optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
Fuzzy time series prediction method based on fuzzy recurrent neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Fuzzy linear regression based on Polynomial Neural Networks
Expert Systems with Applications: An International Journal
Stochastic MIMO channel modelling using FMLP-based inference engine
International Journal of Information and Communication Technology
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Fuzzy neural networks (FNN) as opposed to neuro-fuzzy systems, whose main task is to process numerical relationships, can process both numerical (measurement based) information and perception based information. In spite of great importance of fuzzy feed-forward and recurrent neural networks for solving wide range of real-world problems, today there are no effective training algorithm for them. Currently there are two approaches for training of FNN. First approach is based on application of the level-sets of fuzzy numbers and the back-propagation (BP) algorithm. The second approach involves using evolutionary algorithms to minimize error function and determine the fuzzy connection weights and biases. The method based on the second approach was proposed by the authors and published in Part 1 of this paper [R.A. Aliev, B. Fazlollahi, R. Vahidov, Genetic algorithm-based learning of fuzzy neural networks. Part 1: feed-forward fuzzy neural networks, Fuzzy Sets and Systems 118 (2001) 351-358]. In contrast to the BP and other supervised learning algorithms, evolutionary algorithms do not require nor use information about differentials, and hence, they are most effective in case where the derivative is very difficult to obtain or even unavailable. However, the main deficiency of the existing FNN based on the feed-forward architecture is its adherence to static problems. In case of dynamic or temporal problems there is a need for recurrent fuzzy neural networks (RFNN). Designing efficient training algorithms for RFNN has recently become an active research direction. In this paper we propose an effective differential evolution optimization (DEO) based learning algorithm for RFNN with fuzzy inputs, fuzzy weights and biases, and fuzzy outputs. The effectiveness of the proposed method is illustrated through simulation of benchmark forecasting and identification problems and comparisons with the existing methods. The suggested approach has also been used for real applications in an oil refinery plant for petrol production forecasting.