A novel fuzzy logic system based on N-version programming

For the consideration of different application systems, modeling the fuzzy logic rule, and deciding the shape of membership functions are very critica l issues due to they play key roles in the design of fuzzy logic control sy stem. This paper proposes a novel design methodology of fuzzy logic control system using the neural network and fault-tolerant approaches. The connect ionist architecture with the learning capability of neural network and N-ve rsion programming development of a fault-tolerant technique are Implemented in the proposed fuzzy logic control system. In other words, this research involves the modeling of parameterized membership functions acid the partit ion of fuzzy linguistic variables using neural networks trained by the unsu pervised learning algorithms. Based on the self-organizing algorithm, the m embership function and partition of fuzzy class are not only derived automa tically, but also the preconditions of fuzzy IF-THEN rules are organized. W e also provide two examples, pattern recognition and tendency prediction, t o demonstrate that the proposed system has a higher computational performan ce and its parallel architecture supports noise-tolerant capability. This g eneralized scheme is very satisfactory for pattern recognition and tendency prediction problems.