Topic
Membership function
About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.
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TL;DR: The sampling defuzzifier is compared on aggregated type-2 fuzzy sets resulting from the inferencing stage of a FIS, in terms of accuracy and speed, with other methods including the exhaustive and techniques based on the @a-planes representation.
78 citations
TL;DR: It is found that the property of the system efficiency being equal to the product of the two process efficiencies also holds for the fuzzy case, and this property can be generalized to series systems with more than two processes.
78 citations
Proceedings Article•
01 Jan 2003
78 citations
TL;DR: By rewarding crisp membership degrees, this modified fuzzy clustering approach to building fuzzy models of the Takagi–Sugeno (TS) type automatically from data is applied and some bounds on the approximation quality are given.
78 citations
TL;DR: A wavelet fuzzy neural network using asymmetric membership function (WFNN-AMF) with improved differential evolution (IDE) algorithm is proposed in this study to control a six-phase permanent magnet synchronous motor for an electric power steering (EPS) system.
Abstract: A wavelet fuzzy neural network using asymmetric membership function (WFNN-AMF) with improved differential evolution (IDE) algorithm is proposed in this study to control a six-phase permanent magnet synchronous motor (PMSM) for an electric power steering (EPS) system. First, the dynamics of a steer-by-wire EPS system and a six-phase PMSM drive system are described in detail. Moreover, the WFNN-AMF controller, which combines the advantages of wavelet decomposition, fuzzy logic system, and asymmetric membership function (AMF), is developed to achieve the required control performance of the EPS system for the improvement of stability of the vehicle and the comfort of the driver. Furthermore, the online learning algorithm of WFNN-AMF is derived using back-propagation method. However, degenerated or diverged responses will be resulted due to the inappropriate selection of small or large learning rates of the WFNN-AMF. Therefore, an IDE algorithm is proposed to online adapt the learning rates of WFNN-AMF. In addition, a 32-bit floating-point digital signal processor, TMS320F28335, is adopted for the implementation of the proposed intelligent controlled EPS system. Finally, the feasibility of the proposed WFNN-AMF controller with IDE for the EPS system is verified through experimental results.
78 citations