Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
The Research of Decision Information Fusion Algorithm Based on the Fuzzy Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A saturation binary neural network for crossbar switching problem
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
| Hi-index | 0.00 |
It has been reported through simulations that Hopfield networks for crossbar switching almost always achieve the maximum throughput. It has therefore appeared that Hopfield networks of high-speed computation by parallel processing could possibly be used for crossbar switching. However, it has not been determined whether they can always achieve the maximum throughput. In the paper, the capabilities and limitations of a Hopfield network for crossbar switching are considered. The Hopfield network considered in the paper is generated from the most familiar and seemingly the most powerful neural representation of crossbar switching. Based on a theoretical analysis of the network dynamics, we show what switching control the Hopfield network can or cannot produce. Consequently, we are able to show that a Hopfield network cannot always achieve the maximum throughput