Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Function approximation based on fuzzy rules extracted frompartitioned numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A GA-based method for constructing fuzzy systems directly from numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Evolutional RBFNs prediction systems generation in the applications of financial time series data
Expert Systems with Applications: An International Journal
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Fuzzy rules generation is known an important task in designing fuzzy systems. This article applies an evolutionary fuzzy rules learning scheme to approach desired fuzzy systems having a lower fuzzy rules. The proposed learning scheme overcomes limitations of conventional fuzzy rules generation and completes the complex searching problems to extract the desired fuzzy system. In this article, aggregations of hyper-ellipsoids fuzzy partitions with different sizes and different positions are suggested to approximate the knowledge rule base of fuzzy systems whose membership functions are arbitrarily shaped and flexibly tuned in parameters searching space. Several corresponding parameters in defining the region of such hyper-ellipsoids type membership functions are efficiently selected based on the simple rule extracting technology. Furthermore, the constructed fuzzy system with only two fuzzy rules can be automatically extracted by the evolutional genetic algorithms (GAs) learning scheme with the guide of special fitness function. Finally, both inverted pendulum balance and nonlinear modeling problems are used to illustrate the effectiveness of the proposed method.