A universal scheme of hidden information detection from original signals via wavelet neural networks

A universal mathematical scheme for hidden information detection from various original n-dimensional signals is presented. The key points include the establishment of functional expression and the employment of cascade neural networks. The former indicates the detection of hidden information is dependent upon the shape of whole signals, not the specific values and the sampling number of the signals, the latter refers to the architecture of neural networks consisting of two neural networks, the first extracts main features of signals, and the second detects hidden information from the extracted features. Since wavelet functions play important role in signal analysis and feature extraction, thus wavelet neural networks constitute the first neural networks. The general scheme of feature extraction from original signals in L²(R^M) by wavelet neural networks is presented. The application in signal processing of chemical chromatographic spectra of solution shows satisfactory results.