Research on chemical agent identification model based on wavelet analysis and neural networks

Abstract

Multi-sensor information fusion (MSIF) technology, is being widely applied to the modern military field. For the sake of implementing effectively detection and rapid exact identification for the chemical agent in sea-battlefield, and applying MSIF technology in Naval Ships Chemical Detection (NSCD) field, the NSCD system based on the multi-sensor fusion model is built. At the same time, connecting the wavelet analysis with the neural networks organically, and based on the wavelet transfer and the neural networks, the system of the speedy features extraction and identification for chemical agent - the Neural Networks Distinguishing Chemical Agent (NNDCA) system - is founded. The model of the NNDCA by the returning neural networks with deviation unit and the method of the feature extraction for the chemical agent based on the wavelet analysis are established, the realization idea of the NNDCA system is put forward, and the hardware accomplishment and the software structure of the NNDCA system are discussed. Based on experiments, the experimental and simulated results show: it is feasible that the qualitative and quantitative analyses for the chemical agent with the NNDCA system based on the MSIF technology and the wavelet analysis. The method can remarkably heighten the accuracy and credibility of the measurement results, and the results are of repeatability.