Investigation On Methods Of Extracting Useful Information From Chemical Signal

In this thesis, the possibility of Fourier self-deconvolution(FSD), wavelet transform(WT), artificial neural network(ANN), genetic algorithms(GAs) and their combination to solving problems such as feature extraction of signals, de-noising and resolution of overlapped peaks have been explored. The application fields of FSD, WT, ANN and GAs are enlarged and some new chemometrics methods are founded. The thesis consists of four chapters, and the author's contributions are in the following four aspects:1.Combinating WT and Fourier transform, a new FSD with wavelet transform in frequency domain is presented. Compared other FSD, the new method can effectively resolve overlapped peaks. This is because the module R obtained after FFT for the original signal is similar to appormix CN obtained from WT for the module R in their linearity and peak width.2.A new method of FSD, FSD of the power spectrum of oscillographic signal, is proposed. In this method the oscillographic signal of base solution and depolarizer are used as line shape function and filter function, respectively. The new method can not only increase the relative incision depth, improve sensitivity and the resolution of the overlapped incision, avoid overdeconvolution and underdeconvolution, but also eliminatethe need for choosing line shape function and filter function. This avoid the subjective interference factors and FSD operation is made easy.3. The BP ANN is applied in extracting weak signal and information of oscillographic chronopotentiometric signals from strong noise background. Effects of noise and peak width of signal are studied.4.Genetic wavelet neural network(GWNN) is proposed and applied to thecompression and de-noising of simulated signal and polargraphic signal. The improper selection of network parameters and local optimal solution which often occurs in the training process of WNN is avoided because the parameter of WNN is optimized by GAs. The intellectualized level of artificial neural network applied in chemical signal processing has been improved ulteriorly.