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Journal Information-measuring and Control Systems №5 for 2014 г.
Article in number:
Some properties of the simulation of adaptive neuro-fuzzy systems based on a simplified fuzzy inference
Keywords: fuzzy logic soft computing simplified fuzzy inference adaptive neuro-fuzzy inference system ANFIS training
Authors:
M. V. Bobyr - Dr.Sc. (Eng.), Associate Professor, South-West State University (Kursk). E-mail: fregat_mn@rambler.ru
S. G. Emelyanov - Dr.Sc. (Eng.), Professor, South-West State University (Kursk). E-mail: rector@swsu.ru
N. A. Milostnaya - Ph.D. (Eng.), South-West State University (Kursk). E-mail: nat_mil@mail.ru
Abstract:
For information and measuring and controlling systems decision making under uncertainty is the actual problem. One of the most promising theories to take account of incomplete knowledge is fuzzy logic. It is based algorithms are fuzzy inference based models Mamdani and Sugeno. While a simplified algorithm fuzzy inference from researchers almost no interest. Unfortunately, the traditional fuzzy inference algorithms have several disadvantages, which include the curse of dimensionality, the presence of empty making, etc., which reduce the emergence of virtually any control system. One possible way to improve the performance of traditional algorithms, fuzzy logic inference are soft computing, allowing to compensate not only the above-mentioned shortcomings, but also provide additive system. In connection with this article were analyzed simulation results of ANFIS (Adaptive Neuro-Fuzzy Inference System) operates on the basis of a simplified algorithm for the implementation of soft and hard arithmetic operations. Studies have shown that in some cases it is advisable to use the ANFIS based on a simplified algorithm with hard formulas.
Pages: 4-12
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