INFERENCE AND CONTRADICTORY ANALYSIS FOR BINARY NEURAL NETWORKS

A weak-inference theory and a contradictory analysis for binary neural networks (BNNs). are presented. The analysis indicates that the essen tial reason why a neural network is changing its states is the existen ce of superior contradiction inside the network, and that the process by which a neural network seeks a solution corresponds to eliminating the superior contradiction. Different from general constraint satisfac tion networks, the solutions found by BNNs may contain inferior contra diction but not superior contradiction.