Competitive and cooperative adaptive reasoning with fuzzy causal knowledge

Abstract

In the current literature on knowledge-based diagnostic reasoning, two kinds of knowledge, experiential knowledge and causal knowledge, are generally acquired and widely used. In 1995, Ding and Gupta [8] proposed a new approach to fuzzy neural network-based adaptive reasoning with experiential knowledge. In that paper a fuzzy neuronal model with composite fuzzy MAX-MIN neurons was presented for the acquisition of experiential knowledge and fuzzy reasoning. In this paper, we present an architecture for a dynamic fuzzy causal neural network for dealing with fuzzy causal knowledge for diagnostic problems. The inhibitory and excitatory mechanism of computational neurons are employed to model the complex competitive and cooperative behaviors between disorders in the process of causal fuzzy reasoning.