Incremental leaning algorithm for self-organizing fuzzy neural network

This paper proposed an incremental learning algorithm for self-organizing fuzzy neural networks (ILSFNN) based on extended radial basis function neural networks, which are functionally equivalent to Takagi-Sugeno-Kang fuzzy systems, is proposed. First, a self-organizing clustering approach is used t...

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Bibliographic Details
Main Authors: Xionghui Long, Dan Su, Rong Hu
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Fuzzy logic
Fuzzy neural networks
Heuristic algorithms
Incrementl learning
Neural networks
Partitioning algorithms
Self-organizing
TSK fuzzy reasoning
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Summary:This paper proposed an incremental learning algorithm for self-organizing fuzzy neural networks (ILSFNN) based on extended radial basis function neural networks, which are functionally equivalent to Takagi-Sugeno-Kang fuzzy systems, is proposed. First, a self-organizing clustering approach is used to establish the structure of the network and obtain the initial values of its parameters. then. a hierarchical on-line self-organizing learning paradigm is employed so that not only parameters can be adjusted, but also the determination of structure can be self-adaptive without partitioning the input space a priori. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that the proposed algorithm is superior in terms of simplicity of structure, learning efficiency and performance.
DOI:10.1109/ICCSE.2012.6295029