Detection System, Based On The Mixed Gas Of The Gas Sensor Array

As the advancement of science and technology and the development of industry, the requirements of the detection and analysis of multicomponent gases are on great rise. But existing detection methods cannot meet them. Therefore, the intelligent electronic olfactory system (Electronic Nose) based on gas sensor array and pattern recognition has become the new tendency of gas detection because it can meet the main requirements such as high sensitivity, good selectivity, long-term stability, low cost and so on.In this thesis, the fundamental principle and system constituent of the electronic olfactory system are analyzed and studied; a set of detection system of gas mixture, combined gas sensor array with artificial neural network pattern recognition technology, is designed and constructed. Employing this system, the processing ability and identification results of several preprocessing algorithms and artificial neural network models are compared and analyzed. And finally the following conclusions are arrived:1) Gas sensor array coupled with pattern recognition technology has the good ability to identify the gas species and quantify its concentration. Any type of gas sensor that can respond broadly to a range or class of gases rather than to a specific one can be employed in the electronic olfactory system, which greatly reduces the cost. The principles of processing and analysis of sensor array responses are essentially similar, thus to some extent, the algorithm of pattern recognition is universal.2) Signal preprocessing is a necessary step to improve the performance of the electronic olfactory system. Among the various preprocessing algorithms, array normalization can remove the concentration factor in sensor responses, so it is particularly useful when the gas concentration is of no interest but fine discrimination is required.3) Artificial neural network (ANN), which has the ability of nonlinear mapping, high tolerance and robustness, can more effectively solve problems such as serious nonlinearitybrought by the cross sensitivity of gas sensors, and to a degree, can compensate the sensor drift and environmental noise, helping to improve the precision of gas detection. Back Propagation (BP) neural network and Radial Basis Function (RBF) neural network have their own advantages respectively and their identification capabilities are little different. The learning process of RBF network is much faster and easier than that of BP network, and not existing local minimum areas. Furthermore, it is also found that, compared with the Single-Stage BP network, the Two-Stage BP network (composed of two BP networks) has high ability to identify gas species and quantify its concentration.Artificial neural network together with gas sensors, which is applied to identification, classification, diagnosis and prediction, will further improve the intelligence of gas detection system. At present, most of this type system use micro-computers or microprocessors to perform the function of neural network, and are still under research in the laboratory. But with the development of artificial intelligence and artificial neural network, especially the sharp increase of integration level and speed of neural network chip, the electronic olfactory system employed artificial neural network will develop quickly and be applied widely.