Inductive neural logic network and the SCM algorithm

Abstract

Neural Logic Network (NLN) is a class of neural network models that performs both pattern processing and logical inferencing. This article presents a procedure for NLN to learn multi-dimensional mapping of both binary and analog data. The procedure, known as the Supervised Clustering and Matching (SCM) algorithm, provides a means of inferring inductive knowledge from databases. In contrast to gradient descent error correction methods, pattern mapping is learned by an inductive NLN using fast and incremental clustering of input and output patterns. In addition, learning/encoding only takes place when both the input and output match criteria are satisfied in a template matching process. To handle sparse and/or noisy data sets, we also present a weighted voting scheme whereby distributed cluster activities combine to produce a final output. The performance of the SCM algorithm, compared with alternative systems, is illustrated on three benchmark problems: (1) mushroom classification, (2) sonar return signal recognition, and (3) sunspot time series prediction.