Research Of The Electronic Nose System Based On The Genetic Neural Network

Electronic Nose (EN) is an instrument, which comprises an array of gas sensors with partial specificity and an appropriate pattern recognition system, capable of recognizing simple or complex odours and gases. EN technology is a cross field with the technologies of sensors, electronic technique, computer, pattern recognition. EN is applied wide in the fields of environmental detection, food and chemistry industry etc with its unique function. What's more, it is getting attention with more domains.As an electronic instrument of detecting gas, EN is the combination of gas sensor array and information processing; therefore the research of pattern recognition technology is of great value. The EN is researched roundly in this paper from the four aspects: the basic principle of EN, the implementation program, the choice and improvement of pattern recognition technology in gas detection.The principle, implementation and design though of the EN were generalized overall from the mechanism of biologic olfactory system. After analyzing the pattern recognition methods, the artificial neural network and genetic algorithm was chosen as the gas detection method of the EN system.A set of EN detection system was designed and realized including the gas sensors array, gas distribution device, test hardware and software. The detection principle of gas sensors and guideline of composing sensor array were analyzed emphatically; the gas distribution device was ready for the preparation of mix-gas(H2S, CO, CH4), the concentration ranges of the three gases are 0-100ppm, 0-100ppm, 0-500ppm respectively. The software of data acquisition is the foundation of mix-gas detection.As for the research of the pattern recognition, the neural network based on the back propagation (BP) neural network was applied in the mix-gas detection, the qualitative and quantitative analysis showed that the neural network can used in the mix-gas detection. The shortage of BP algorithm and the revised measure were analyzed. The BP algorithm is the local search method based on the gradient descent, to overcome the local search character,the genetic algorithm was applied to optimize the BP neural network.The genetic algorithm optimizing neural network was put forward to optimize the neural network weigh coefficient with two steps. The first step was to embed the neural network with genetic algorithm, searching the optimum individual in the approximate range of neural network weigh coefficient; the second step was to use the optimum individual as the initial weigh value, then training the network. The experiment shows that the genetic algorithm optimizing neural network can combine with the global search character of genetic algorithm and local search character of BP algorithm. The genetic neural network can accelerate the speed of convergence (the iterative number descended form 144 to 52) and improve the accuracy (the average error of three gases descended from 6.54ppm,6.82ppm,28.83ppm to 4.64ppm,4.37ppm,17.13ppm respectively).An improved self-adaptive mutation of genetic algorithm was designed with the theory analysis of the parameters of genetic algorithm affecting the work performance. The self-adaptive mutation operate has the ability of jumping over the local minimum point and maintaining the diversity of population to realize the global search. Accordingly the modified self-adaptive mutation operate was put forward. It was a self-adaptive algorithm based on evolution efficiency, during computing the efficiency, the mutation ratio and mutation quantity can self adapt. The algorithm can be only thought about the evolution generations, which is simple and easy to manipulate. Applying the modified algorithm into the mix-gas detection shows that the network accelerates the convergence performence (the success rate increased from 40% to 75%) and improves the detection accuracy (the average error of three gases descended to 3.72ppm, 4.22ppm, 15.78ppm respectively).