Exponential Bidirectional Associative Memory Model with Intraconnection
具有内连接的指数多值双向联想记忆模型

C_C Wang's multi_valued exponential bidirectional associative memory model (MVeBAM) is a neural network with higher storage capacity. In this paper, based on the MVeBAM, we propose a new multi_valued exponential bidirectional associative memory model with intraconnection (EMVeBAM) by adding an auto_correlation term (or an intraconnection) to the exponents, extending the MVeBAM. The stability of the proposed model is proven in synchronous and asynchronous update modes with a defined energy function, which ensures that the learnt patterns become stable points of the EMeBAM. Finally, the computer simulation results verify that the EMVeBAM has higher storage capacity and better error_correcting capability than those of MVeBAM.