Structural simplification of FMLP

Author

Ai, Fangju

Author_Institution

Lab. for Automated Reasoning & Programming, Chengdu Inst. of Comput. Application, China

Volume

2

fYear

2005

Lastpage

952

Abstract

The number of fuzzy rules directly determines the complexity and efficiency of a fuzzy multilayer perceptron (FMLP). Based on the neural network self-configuring learning (NNSCL) algorithm, the NNSCL-I algorithm is obtained by using the generalized inverse matrix (GIM) algorithm to adjust the remaining weights after pruning neurons. The NNSCL-I algorithm is applied in the rule-reasoning layer of the FMLP to simplify its rules and structure with no degradation in the original performance. Experimental results show the effectiveness and the feasibility of the algorithm.

Keywords

fuzzy neural nets; learning (artificial intelligence); matrix inversion; multilayer perceptrons; planning (artificial intelligence); self-organising feature maps; FMLP; GIM algorithm; NNSCL-I algorithm; complexity; fuzzy multilayer perceptron; fuzzy rules; generalized inverse matrix; neural network self-configuring learning; rule-reasoning layer; structural simplification; Computer applications; Dispersion; Feedforward neural networks; Fuzzy set theory; Laboratories; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Circuits and Systems, 2005. Proceedings. 2005 International Conference on

Print_ISBN

0-7803-9015-6

Type

conf

DOI

10.1109/ICCCAS.2005.1495265

Filename

1495265