Discrete Hopfield neural network calculation optim
2016-08-23
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Using neural networks for solving combinatorial optimization problems is an important aspect of neural networks. The so-called combinatorial optimization problems, that is, under the given constraints, objective function minimum (or maximum) variable problem. The Hopfield network is applied for solving combinatorial optimization problems, objective functions into the network's energy function variables correspond to the status of the network problem, so that when the network's energy function converges to the minimum value, the optimal solution of the problem is obtained. Neural network parallel computation, calculation, not as the number of dimensions increases exponentially, "bang", particularly effective for optimization of high-speed computing. By continuous Hopfield neural networks for optimization of the objective function and constraints into energy that corresponds to the function that will correspond to the variables of the problem state of the neurons in the neural
matlab
神经网络
hopfield
优化
计算
问题
连续
旅行
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