New distinguishing method of prototype stability about associative Hopfield neural networks

In view of discrete Hopfield neural networks based on Hebb learning law, the essential method of achieving association and memory function and the essential thinking of prototype stability are discussed. According to the existing problem, the necessary and sufficient conditions of distinguishing prototype stability were put forward. A sufficient condition of distinguishing prototype stability was given. This makes it very easy to distinguish prototype stability. At last, the matrix form of distinguishing prototype stability was given. All theorems and deductions have been proved.