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Modeling chaotic two-dimensional mapping with fuzzy-neuron networks

✍ Scribed by I-Cheng Yeh

Publisher: Elsevier Science
Year: 1999
Tongue: English
Weight: 438 KB
Volume: 105
Category: Article
ISSN: 0165-0114
DOI: 10.1016/s0165-0114(97)00263-7

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✦ Synopsis

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel neural network architecture based on fuzzy set theory, fuzzy-neuron network (FNN), and tests its efficiency and accuracy in modeling chaotic two-dimensional mapping. The FNN architecture is a standard back-propagation neural network; however, fuzzy neurons are added to the network. The network is tested using two classic chaotic dynamic systems: Ikeda mapping and Jin mapping. Experimental results demonstrate that the fuzzy neurons in the network provide an enhanced network architecture and improve the performance of these networks significantly.

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