Studies on using the higher order neural network for pattern recognition and optimization
Jinyan Li, Ying Lin Yu, Wangchao Li · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
The higher order neural network used for pattern recognition and optimization is studied in this paper and the results in two different aspects have been obtained. (1) Theoretically, the capacity formula of the Hopfield neural network with the second order weights has been obtained. Compared with the first order network, the capacity of the second order network is about three times greater than that of the first order one and the cost to reach such an efficiency is to add higher order weights. The simulated experiments according to the theory by digital computer are satisfied. (2) Theoretically, the method of how to solve the optimization problems, whose energy functions are more general than Lyapunov function, has been put forward at the end of this paper.