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Nonlinear programming with feedforward neural networks

机译:前馈神经网络的非线性编程

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We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than the optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf-optimization tool.
机译:我们通过在耦合神经网络/目标函数表示中连续训练多层前馈神经网络,提供了一种解决约束优化问题的实用有效方法。非线性编程问题很容易映射到该表示形式中,该表示形式比使用类Hopfield网络进行的优化方法更简单,更透明,并且对出现在函数中的功能提出了非常温和的要求。给出了仿真结果,并与现成的优化工具进行了比较。

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