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Full-Text Articles in Physical Sciences and Mathematics
Improving The Hopfield Network Through Beam Search, Tony R. Martinez, Xinchuan Zeng
Improving The Hopfield Network Through Beam Search, Tony R. Martinez, Xinchuan Zeng
Faculty Publications
In this paper we propose a beam search mechanism to improve the performance of the Hopfield network for solving optimization problems. The beam search readjusts the top M (M > 1) activated neurons to more similar activation levels in the early phase of relaxation, so that the network has the opportunity to explore more alternative, potentially better solutions. We evaluated this approach using a large number of simulations (20,000 for each parameter setting), based on 200 randomly generated city distributions of the 10-city traveling salesman problem. The results show that the beam search has the capability of significantly improving the network …
Improved Hopfield Networks By Training With Noisy Data, Fred Clift, Tony R. Martinez
Improved Hopfield Networks By Training With Noisy Data, Fred Clift, Tony R. Martinez
Faculty Publications
A new approach to training a generalized Hopfield network is developed and evaluated in this work. Both the weight symmetricity constraint and the zero self-connection constraint are removed from standard Hopfield networks. Training is accomplished with Back-Propagation Through Time, using noisy versions of the memorized patterns. Training in this way is referred to as Noisy Associative Training (NAT). Performance of NAT is evaluated on both random and correlated data. NAT has been tested on several data sets, with a large number of training runs for each experiment. The data sets used include uniformly distributed random data and several data sets …