Research On Theory Analysis And Applications For Critical Characteristics Of Pulse Coupled Neural Network

Pulse Coupled Neural Network (PCNN) is a new generation of artificial neural network simulated the mechanism of information processing in the neurons of mammalian visual cortex. The PCNN has been successfully applied in image processing and other areas, and shows the unique superiority. However, the applications using PCNN always face the difficulty of neuron parameters estimation due to lack of fairly through theoretical analysis for PCNN. So this dissertation will theoretical analyze several characteristics for PCNN, and apply the analysis results to some practical applications. On the whole, the content and progress of the research work mainly in the following four aspects.First, in the PCNN and the simplified PCNN (S-PCNN), the dynamic characteristics of neurons have been qualitatively analyzed. In a situation that a neuron without external coupling pulses firing self-stimulating pulses, the mathematical descriptions of the self-stimulating pulse period for PCNN and S-PCNN neurons, of the initial firing-pulse time phase in the state self-stimulating pulse period for PCNN neurons, and of the pulse time phase for S-PCNN neurons, have been obtained. On the other hand, in a situation that a neuron receives external coupling pulses, the mathematical descriptions of the captures-period and the refractory-period have been obtained. The experimental simulation to verify the correctness of the analysis conclusions.Second, based on the analysis of the dynamic characteristics, analyzed the pulse statistical characteristics of neurons, and applied the theoretical analysis results to face recognition and color image enhancement. In the theoretical process, around two pulse statistical methods:Oscillation time sequences (OTS) and pulse oscillation frequency map (OFG), analyzed the invariance of the OTS, and divided the OTS into self-stimulating OTS (S-OTS) and captured OTS (C-OTS). Analyzed the clustering characteristics of regional features for the OFG, and using OFG sequences (OFGS) as a description of the evolution process of features clustering. Given a PCNN neuron parameters estimation method. In the practical applications, using the OTS in PCNN to realize face recognition, and using the X-OTS (OTS, C-OTS and S-OTS) in S-PCNN to realize face recognition. Based on S-PCNN and OFGS, combined with high-dimensional space vector operation, proposed an effective color image enhancement algorithms. Simulation results verify the validity of the applications.Third, based on the analysis of the dynamic characteristics, analyzed the pulse oscillation correlative characteristics of neurons, and applied the theoretical analysis results to image segmentation and pulse noise filtering. In the theoretical process, given the necessary conditions of S-PCNN neurons firing synchronous pulses, and then obtained the causes of PCNN neuron realizing pulse synchronous oscillation. Analyzed the temporal correlations of neurons pulsing, and the limitation of using pulse oscillation time phase as the description feature clustering, and analyzed the reasonableness of using pulse oscillation frequency as the description of feature clustering. In the practical applications, based on the pulse oscillation frequency in PCNN, proposed an efficient image segmentation method with a neuron parameters estimation method. Used the synchronous pulse oscillation correlation in S-PCNN to realize impulse noise detection wih a neuron parameters estimation method and a filtering method called extended window median filter (EWMF). Simulation results verify the validity of the applications.Fourth, based on the analysis of the dynamic characteristics, analyzed the pulse wave propagation characteristics of neurons, and applied the theoretical analysis results to multi-constrained QoS route problem. In the theoretical process, given the environment conditions and the parameters conditions constrained for occurring pulse wave propagation in S-PCNN, then based on the parameters condition constrained, using experimental methods to analyze the influences on pulse wave propagation characteristics for parameters. In the applications of the theoretical results, in order to solve route optimization using pulse wave propagation, proposed competitive pulse coupled neural network (CPCNN) model, and derived parameters condition constrained for neurons, and then combined with the theory of pulse task generation, decomposition and state transition, implemented the solution of multi-constrained QoS route problem. Simulation results verify the validity of the applications.