基于自适应遗传小波神经网络的水质评价建模

水环境污染过程的非确定性和非线性,使得传统的水质评价方法存在局限性。为了提高水质评价的准确性,提出了一种基于改进小波神经网络(wavelet neural network,WNN)的水质评价模型。采用自适应遗传算法(adaptive genetic algorithm,AGA)对小波神经网络的初始权值进行优化,再通过小波神经网络算法对网络进行训练,最后对训练好的网络展开测试。仿真结果表明,自适应遗传算法和小波神经网络的结合提高了网络的训练效率,该方法可以用于水质评价建模,并且评价结果具有较高的精度和准确性。

MODEL FOR WATER QUALITY EVALUATION BASED ON WAVELET NEURAL NETWORK OF ADAPTIVE GENETIC ALGORITHM

The nonlinearity and uncertainty of water environmental pollution make the traditional water quality evaluation methods have limitations． In order to improve the accuracy of water quality evaluation，the paper put forward a water quality evaluation model based on improved wavelet neural network ( WNN) ． The initial weights of the wavelet neural network was optimized based on adaptive genetic algorithm ( AGA) ，and then training the network was trained by using the wavelet neural network algorithm，and finally，the trained network was tested． The simulation results showed that the combination of adaptive genetic algorithm and wavelet neural network improved the efficiency of network training， and this method could be used in for model of water quality evaluation，and the evaluation result would have higher precision and accuracy．