Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Spreading Associative Neural Network Recognizes the Shape and Position of an Object Simultaneously
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Lyapunov theory-based multilayered neural network
IEEE Transactions on Circuits and Systems II: Express Briefs
CMOS current-mode neural associative memory design with on-chip learning
IEEE Transactions on Neural Networks
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A CMOS ratio-memory cellular nonlinear network (RMCNN) requiring no elapsed time is proposed. The correlations between any two neighboring cells are stored in the memories. The ratio weights of each cell are generated through a comparison of the four correlations around one cell with the mean value of these four correlations. With this method, the elapsed time required by the previously existing RMCNN algorithm is no longer required and, therefore, the ratio weights can be generated individually. Moreover, the use of multi-dividers can be avoided to make the circuit simple. Based on the proposed algorithm, a CMOS RMCNN chip requiring no elapsed time has been designed and fabricated using TSMC O.35-µm 2P4M mixed-signal technology. In the fabricated chip, three test patterns can be learned and recognized.