Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
An Efficient Fuzzy C-Means Clustering Algorithm
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rule-based modeling: fast construction and optimal manipulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On stability of fuzzy systems expressed by fuzzy rules with singleton consequents
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy network to generate human-understandable knowledge from data
Cognitive Systems Research
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
An improved fuzzy neural network based on T-S model
Expert Systems with Applications: An International Journal
Fuzzy neural network structure identification based on soft competitive learning
International Journal of Hybrid Intelligent Systems
Image backlight compensation using neuro-fuzzy networks with immune particle swarm optimization
Expert Systems with Applications: An International Journal
Applying fuzzy method to vision-based lane detection and departure warning system
Expert Systems with Applications: An International Journal
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Improving project-profit prediction using a two-stage forecasting system
Computers and Industrial Engineering
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we propose a self-adaptive neural fuzzy network with group-based symbiotic evolution (SANFN-GSE) method. A self-adaptive learning algorithm consists of two major components. First, a self-clustering algorithm (SCA) identifies a parsimonious internal structure. An internal structure is said to be parsimonious in the sense that the number of clusters (fuzzy rules) is equal to the true number of clusters in a given training data set. The proposed SCA is an online method and is a distance-based connectionist clustering method. Unlike the traditional cluster techniques that only consider the total variation to updates the only one mean and deviation. The proposed SCA method considers the variation of each dimension for the input data. Second, a group-based symbiotic evolution learning (GSE) method is used to adjust the parameters for the desired outputs. The GSE method is different from traditional GAs (genetic algorithms), with each chromosome in the GSE method representing a fuzzy system. Moreover, in the GSE method, there are several groups in the population. Each group represents a set of the chromosomes that belong to a cluster computing by the SCA. In this paper we used numerical time series examples (one-step-ahead prediction, Mackey-Glass chaotic time series, and sunspot number forecasting) to evaluate the proposed SANFN-GSE model. The performance of the SANFN-GSE model compares excellently with other existing models in our time series simulations.