Nonlinear discriminant functions and associative memories
AIP Conference Proceedings 151 on Neural Networks for Computing
The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning using distance based training algorithm for pattern recognition
Pattern Recognition Letters
IEEE Transactions on Neural Networks
Injecting Chaos in Feedforward Neural Networks
Neural Processing Letters
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A sparse two-Dimension distance weighted approach for improving the performance of exponential correlation associative memory (ECAM) and modified exponential correlation associative memory (MECAM) is presented in this paper. The approach is inspired by biological visual perception mechanism and extensively existing sparse small-world network phenomenon. By means of the approach, the two new associative memory neural networks, i.e., distance-based sparse ECAM (DBS-ECAM) and distance-based sparse MECAM (DBS-MECAM), are induced by introducing both the decaying two-Dimension distance factor and small-world architecture into ECAM and MECAM's evolution rule for image processing application. Such a new configuration can reduce the connection complexity of conventional fully connected associative memories so that makes AM' VLSI implementation easier. More importantly, the experiments performed on the binary visual images show DBS-ECAM and DBS-MECAM can learn and recognize patterns more effectively than ECAM and MECAM, respectively.