Study On Method Of Mixed Gas Recognition Based On Improved Wavelet Neural Network Algorithm

As the living level of our people approved,more and more people spend their money on fitting up. But there are many polluted gases in the materials. These gases are harmful to the healthy of people. So it is very important to inspect the concentrations of these gases. This paper did research work for the method of inspection of these polluted gases.In this paper, a gas sensor array was used to collect signals. Because wavelet neural network has the following merits: high precision, learning rate fast etc, we use wavelet neural network in the field of inspection of these polluted gases.In the basis of one dimension wavelet neural network,we researched two different structures of wavelet neural networks. And we used them into the inspection of these polluted gases. We used them because the constringent speed of wavelet neural network is very fast, they are not sensitive to the inputs of the network and they have the characteristic that they also can effectively approach the functions or signals.Classical neural networks mostly train the network with back propagation algorithm. But the back propagation algorithm often gets into the minimum value and its constringent speed is slow. Aim at the gases, we design a optimum back propagation algorithm. We improved the Classical back propagation algorithm on two points: one is that we adopt a method of self-adaptive learning rate algorithm that is based on the sign change of the grads, second is that we adopt momentum item.Finally we contrast the result of the experiment, and find that the optimum algorithm can effectively improve the training speed of wavelet neural network and avoid that algorithm get into minimum values. The experiment proved that we can well deal with the gas signals with wavelet neural network. The inspection of these polluted gases result is satisfying.