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- W2218872326 abstract "In this study, we propose a neural network model which generalizes the Hopfield neural network model. Like the original model the proposed model has the convergence property. It can model application problems that can not be modeled using the original Hopfield neural network.We then study two major applications of the proposed model: (1) Associative memories. Associative memories are of great use in many areas, such as pattern recognition, signal processing, and machine learning. In using the proposed neural network as associative memories, two important issues have to be addressed: (a) efficient and effective construction of neural associative memories, (b) the memory capacity of such associative memory. Our results show that the proposed neural network increases the memory capacity. We obtained a good upper bound on the capacity of such neural associative memory using the worst case analysis. Other issues of the neural associative memory, such as the parasite memory, nearest convergence are also studied. (2) Combinatorial optimization problems. We study the Traveling Salesman Problem using the proposed neural network model. The proposed neural network model allows many effective adaptive techniques to escape from local optimums (one important reason why many previous researches did not succeed using Hopfield neural network). The results are very encouraging. The new neural algorithm has performance comparable not only to other neural algorithms, but also to some of the best conventional heuristic algorithms, such as Lin & Kernighan algorithm. The new neural algorithm also scales better than the Lin & Kernighan algorithm." @default.
- W2218872326 created "2016-06-24" @default.
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- W2218872326 date "1990-01-01" @default.
- W2218872326 modified "2023-09-27" @default.
- W2218872326 title "A neural network model, its properties and applications" @default.
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