Efficient fuzzy partition of pattern space for classification problems
Fuzzy Sets and Systems - Special issue on fuzzy data analysis
A course in fuzzy systems and control
A course in fuzzy systems and control
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
Fuzzy transforms method and attribute dependency in data analysis
Information Sciences: an International Journal
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
An evolving-onstruction scheme for fuzzy systems
IEEE Transactions on Fuzzy Systems
Identification of transparent, compact, accurate and reliable linguistic fuzzy models
Information Sciences: an International Journal
A hierarchical model of a linguistic variable
Information Sciences: an International Journal
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Logic-Based Fuzzy Neurocomputing With Unineurons
IEEE Transactions on Fuzzy Systems
Approximation theory of fuzzy systems-SISO case
IEEE Transactions on Fuzzy Systems
Approximation theory of fuzzy systems-MIMO case
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
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This paper proposes the Simplified Structure Evolving Method (SSEM) for fuzzy system identification, which improves our earlier work on the Structure Evolving Learning Method for fuzzy systems (SELM). The improvement is that SSEM applies a scheme that starts with the simplest fuzzy rule set with only one fuzzy rule (instead of 2^n fuzzy rules as in SELM, where n is the number of input variables), whilst retaining all the advantages of SELM. SELM is able to solve the problem of the exponential increase of fuzzy rules, however, it requires a basic fuzzy rule set which is exponential to the number of input variables (2^n fuzzy rules) as a starting point. The improvement offered by SSEM enables automatic feature selection and system structure identification, and avoids inefficient rules and inefficient variable involvement for system identification. This improvement enables fuzzy systems to be applicable to problems of any input dimension. Three benchmark examples with high dimension inputs are given to illustrate the advantages of the proposed algorithm.