The Application To Classifying Apples Using Electronic Nose Based On Nonlinear Pattern Recognition

In this thesis, our attention was paid to the principle and construction of gas sensor arrays, and the new methods of pattern recognition (PR) achieved by genetic algorithms (GA) and neural networks (NN) in the processing of electronic nose. On the study of the extensive reference in the field of electronic nose, we had developed an electronic nose equipment. Such equipment could classify the apples by odor. The main contents and methods of this thesis are:1. The basic form of gas sensor array in electronic nose was given. The electronic nose equipment was developed and the new test processing was given too. The temperature, humidity and velocity of flow of gas carrier was controlled in this new equipment.2. Using MATLAB, four characters (max, time, max-gradient and integral) from each curve of gas sensors had been picked-up. And we got 16 characters data from each sample.3. The PR model of GA based on Binary-Tree Coding was established, and was programmed with VC++. A new method named steady routlette wheel selection was created. Good result was obtained.4. The model of BP optimized by GA and the model of RBF optimized by GA wase established, and were programmed with VC++. Good result was obtained.The following conclusions were obtained according to the research described above: the PR model of GA based on Binary- Tree Coding, the model of BP optimized by GA and the model of RBF optimized by GA had obtained good result in classifying the apples.