Analysis and optimal design of continuous neural networks with applications to associative memory

The asymptotic stability of a continuous neural network is analyzed for associative memory. An optimal design method is proposed which ensures the highest associative memory speed and guarantees the storage of each desired memory with attractivity. The network asymptotic stability is analyzed by means of a new energy function, and four theorems are obtained. By comparing these theorems with existing ones, it can be shown that in some cases they are consistent, while in others they are not equivalent but complementary to each other. Further study results in two more generalized conclusions, of which the existing conclusions are special cases. The network optimal design method is proposed in terms of an optimal associative memory theorem. Two application examples are presented to demonstrate the defeffectiveness of the optimal design method, which can be used to design the network for many applications.