System for converting neural network to rule-based expert system using multiple-valued logic representation of neurons in feedforward network

Abstract

A computer-implemented apparatus and method for generating a rule-based expert system from a trained neural network which is expressed as network data stored in a computer-readable medium. The rule-based expert system represents an interconnected network of neurons with associated weights data and threshold data. A network configuration extractor is provided for accessing the network data and for ascertaining the interconnection structure of the trained neural network by examining the network data. A transformation system is utilized to alter the algebraic sign of at least a portion of the weights data to eliminate differences in the algebraic sign among the weights data while selectively adjusting the threshold data to preserve the logical relationships defined by the neural network. A symbolic representation generator applies a sum-of-products search upon each neuron in the network to generate a multivalued logic representation for each neuron. A propagation mechanism combines the multivalued logic representation of each neuron through network propagation to yield a final logical expression corresponding to a rule-based expert system of the trained neural network. The resulting apparatus permits the knowledge incorporated in the connection strengths of neurons to be expressed as rule-based expert system.

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