用神经网络和遗传算法优化电镀锌镍磷工艺参数

Optimization of Zn-Ni-P Alloy Electroplating Technological Parameter Based on ANN and GA

提出了一种神经网络与遗传算法相结合的电镀锌镍磷合金工艺参数优化方法。以试验数据为样本,通过神经网络建立电镀工艺参数与电镀性能关系之间的复杂模型,利用遗传算法对电镀工艺参数进行优化,可充分发挥神经网络的非线性映射能力和遗传算法的全局寻优能力。试验显示了方法的有效性和优越性。

A method of Zn-Ni-P electroplating alloy technological parameter optimization was presented by combining neural networks with genetic algorithm( GA). Taking experiment data as samples, the model between electroplating technological parameter and electroplating function was established based on artificial neural networks ( ANN ) , and then the electroplating technological parameters were optimized with genetic algorithm. It fully played their function which ANN had nonlinear shine and GA had overall search. The test showed its effectible and advantage.