Abstract
The paper proposes a distributed computational structure called knowledge-based network as a classification scheme in pattern recognition. Unlike the existing architectures and algorithms of pattern recognition the network allows for an explicit representation of domain classification knowledge while maintaining its learning capabilities. The knowledge-based network blends useful properties of knowledge-based systems (namely explicit knowledge representation) with those advantageous for neural networks (viz. learning). The network is composed of basic AND and OR neurons. Fuzzy clustering constitutes a preprocessing phase leading towards developing geometric constructs. They contribute to a conceptual level around which numerical processing of the classifier is centred.