Study On Electronic-Nose Recognition Method With Independent Component Analysis And Neural Network

As the development of environmental science and the environmental detection technique, the cheap measuring gas sensors with good performance have become the new direction of measurement field. Caused by the physical shortcomings of gas sensors, it is impossible for a single gas sensor to identify multiple gases. So the electronic nose techniques based on gas sensor array and pattern recognition is becoming an important way in dealing with cross-sensitivity in gas analysis. The pattern recognition technology plays the crucial role to the behavior of electronic nose.The research mainly focuses on recognition technology of independent component analysis and neural network. It is important to introduce the foundation and independent criterion of the independent component analysis. FastICA(Fast independent component analysis) is brought and applied to data pre-processing in the electronic nose system. This pre-processed data is given a good classification for the gases. The data is inputted in the BP (back propagation) NN (neural network), then do simulation .The original data is get from gas sensors array that is composed of six gas sensors. The 30 gas samples of different density CO,CH₄ and H₂ is carried on the qualitative identification and the 30 gas samples of different density CH4 is carried on the quantitative detection.The simulation results show that the pattern recognition technology which independent component analysis and neural network are unified is feasible in the electronic nose system. The data of per-processing with FastICA is eliminated the data correlation, and then the configuration of BP neural network obtained the simplification, the network convergence rate is quicker, moreover the recognition accuracy is higher. The qualitative identification rate reaches 100%, the quantitative detection rate reaches 97.19%.