基于遗传神经网络的混合气体识别研究

针对误差反向传播(BP)算法和遗传算法各自的优点和不足,提出了遗传算法优化神经网络技术:利用遗传算法的全局搜索能力,对神经网络连接权进行优化,以遗传算法优化的初值作为BP神经网络的初始权值,再用BP算法训练网络.优化后的BP网络其误差的递减速度和收敛速度都比标准BP网络快,而且对学习速率调整要求更少.将遗传神经网络应用于混合气体定量识别的训练中,得到的最大误差由20.7 %降为12.1 %,平均误差从5.4 %降为3.5 %,识别效果得到了提高.

Recognition of multi-gas by using genetic algorithm optimizing neural network

A genetic algorithm optimizing neural network(GA-NN) is given,after genetic algorithm and back-propagation(BP) neural network were studied.Optimizing the weights of neural network with the character of local search ability of genetic algorithm,the optimized value was used as the initial weights of the back-propagation neural network,and then the network was trained by the back-propagation method.The results show that the convergence speed and precision of genetic algorithm optimizing neural network are better than that of the single algorithm.The application of genetic algorithm optimizing neural network to the recognition of multi-gas validates that the method improved the detection effect of multi-gas with reducing the maximal error and average error from 20.7 % and 5.4 % to 12.1 % and 3.5 %.