Multivalued Exponential Multidirectional Associative Memory
多值指数式多向联想记忆模型

MDAM (multidirectional associative memory) is a direct extension of Kosko BAM (bidirectional associative memory). It can be applied in data fusion and splitting larger dimensional input patterns to ease some problems to be solved. At present, the existing multidirectional models only dealt with binary input-output patterns or data. However, some patterns in such applications as image processing and pattern recognition are represented in a multivalued mode. Therefore, the above models have some processing difficulties. The purpose of this paper is to present a MVeMDAM (multi-valued exponential associative memory) to partially solve the difficulties. In this paper, the stability of the MVeMDAM is proven in synchronous and asynchronous update modes for neuron states, which enables the MVeMDAM to ensure all the training pattern sets to become the stable states of the system. Finally, the computer simulation results confirm feasibility of the proposed model.