Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
Adaptive Neuro-Fuzzy Networks with the Aid of Fuzzy Granulation
IEICE - Transactions on Information and Systems
Linguistic models and linguistic modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Development of Incremental Models
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
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This paper is concerned with a method for combining global model with local adaptive neuro-fuzzy network. The underlying principle of this approach is to consider a two- step development. First, we construct a standard linear regression as global model which could be treated as a preliminary design capturing the linear part of the data. Next, all modeling discrepancies are compensated by a collection of rules that become attached to the regions of the input space in which the error becomes localized. The incremented neurofuzzy network is constructed by building a collection of information granules through some specialized fuzzy clustering, called context-based fuzzy c-means that is guided by the distribution of error of the linear part of its development. The experimental results reveal that the proposed method shows a good approximation and generalization capability in comparison with the previous works.