Fuzzy sets-based neural network for pattern understanding
YU Wen-xian, Jun Lü, Jianhui Wu, Guo Guirong · 2002
A fuzzy classification process model and a rational neural network topology are suggested and studied. A new method of constructing membership functions is proposed by using a self-organizing feature map network, kernel estimation of the probability distribution, and a consistent transformation between probability and possibility. Sugeno's (1974) fuzzy integral is briefly reviewed. Then, an improved fuzzy integral, which is based on double set measures, is proposed. The corresponding classification neural network is underlined and analyzed. This fuzzy set-based neural network can combine fact-level information with knowledge-level information consistently, and its classification process is almost identical to the human cognitive process. The given test results show that simultaneously high levels of robustness and accuracy for radar ship classification have been reached by using the proposed fuzzy set-based neural network.>