A Fuzzy-Possibilistic Neural Network to Clustering

Abstract

advantageous over crisp clustering in some applications such as pattern recognition, image segmentation, and compression. In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed to clustering problem. The main purpose is to modify the Hopfield network and embed Fuzzy Possibilistic Fuzzy C-Means (FPCM) method to construct a classification system named Fuzzy-Possibilistic Hopfield Net (FPHN). The classification system is paradigms for the implementation of fuzzy logic systems in neural network architecture. Instead of one state in a neuron for the conventional Hopfield nets, each neuron occupies 2 states called membership state and typicality state in the proposed PFHN. The proposed network not only solves the noise sensitivity fault of Fuzzy C-Means (FCM) but also overcomes the simultaneous clustering problem of Possibilistic C-Means (PCM) strategy. In addition to the same characteristics as the possibilistic fuzzy c-means algorithm, the designed neural-network-based approach is self-organized structure that is highly interconnected and can be implemented in a parallel manner. The experimental results show that the proposed FPHN can obtain promising solutions