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 essential reason why a neural network is changing its states is the existence 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 satisfaction networks, the solutions found by BNNs may contain inferior contradiction but not superior contradiction.