Efficient Electronic Nose Based on Convolutional Neural Network Modular Search Multi Gas Classification Algorithm
This paper designs a sensor signal mapping method based on Gram angle and field,and proposes a convolutional neural network modular structure search method(block GS)based on AlexNet.The experimental results show that the block GS method can search for high-performance network structures,with classification accuracies of 92.11%and 93.33%on two gas datasets,respectively,which is nearly 5%higher than ordinary grid search.This method is expected to become one of the effective solutions for the design of electronic nose pattern recognition algorithms.
electronic nosegraham point and fieldconvolutional neural networkgrid searchgas classification algorithm
万方数据
维普
