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Information Technology Journal
  Year: 2007 | Volume: 6 | Issue: 5 | Page No.: 704-710
DOI: 10.3923/itj.2007.704.710
 
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A Class of New Fuzzy Inference Systems with Linearly Parameter Growth and Without Any Rule Base

Zhang Dianyou, Wang Shitong, Han Bin and Hu Dewen

Abstract:
In this study, a class of new fuzzy inference systems New-FISs is presented. Compared with a standard fuzzy system, New-FIS is still a universal approximator and has the following preferred advantage: no any fuzzy rule base and linearly parameter growth. Thus, New-FIS effectively overcomes the second curse of dimensionality: there is an exponential growth in the number of parameters of fuzzy system as the number of input variables, resulting in surprisingly reduced computational complexity and being especially suitable for applications, where the complexity is of the first importance with respect to the approximation accuracy.
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How to cite this article:

Zhang Dianyou, Wang Shitong, Han Bin and Hu Dewen, 2007. A Class of New Fuzzy Inference Systems with Linearly Parameter Growth and Without Any Rule Base. Information Technology Journal, 6: 704-710.

DOI: 10.3923/itj.2007.704.710

URL: https://scialert.net/abstract/?doi=itj.2007.704.710

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