Concerning the problems of slow convergence and low accuracy of software quality prediction model based on Back Propagation Neural Network （BPNN）， a Software Quality Prediction method based on BPNN optimized by Ant Colony Optimization algorithm （SQP-ACO-BPNN） was proposed. Firstly， the software quality evaluation factors were selected and a software quality evaluation system was determined. Secondly， BPNN was adopted to build initial software quality prediction model and ACO algorithm was used to determine network structures， initial connection weights and thresholds of network. Then， an evaluation function was given to select the best structure， initial connection weights and thresholds of the network. Finally， the network was trained by BP algorithm， and the final software quality prediction model was obtained. Experimental results of predicting the quality of airborne embedded software show that the accuracy， precision， recall and F1 value of the optimized BPNN model are all improved with faster convergence， which indicates the validity of SQP-ACO-BPNN.

In order to mitigate the network congestion by link failures, a new survivability evaluation method named SASFL 〖BP(〗(Survivability Algorithm based on Shuffled Frog Leaping, )〖BP)〗 was proposed by shuffled frog leaping algorithm and wavelet technology. In this method, the evaluation index of survivability was presented at first, and wavelet transform was used to decompose the arrivel flow in failures state. Then, the optimization wavelet coefficients with shuffled frog leaping was reconstructed to network remained traffic. Finally, simulation was conducted to study the relationship between network survivability and failures link, as well as weight factor with OPNET and Matlab. Compared with the other methods, SASFL algorithm has better adaptability.

Feature extraction and similarity measurement for graphs are important issues in computer vision and pattern recognition. However, traditional methods could not describe the graphs under some non-rigid transformation adequately, so a new graph feature descriptor and its similarity measurement method were proposed based on Graph Context (GC) descriptor. Firstly, a sample point set was obtained by discretely sampling. Secondly, graph context descriptor was presented based on the sample point set. At last, improved Earth Mover's Distance (EMD) was used to measure the similarity for graph context descriptor. Different from the graph edit distance methods, the proposed method did not need to define cost function which was difficult to set in those methods. The experimental results demonstrate that the proposed method performs better for the graphs under some non-rigid transformation.

Considering the problem that the abnormal features have great similarity so that simple structure and high precision modeling cannot be achieved, a control chart pattern recognition method based on Kernel Principal Component Analysis (KPCA) and neural network was proposed. Firstly, the kernel method was used to translate the nonlinear feature into a higher dimensional linear feature space. Secondly this feature was projected to lower dimensional feature space. Finally the BP neural network classifier was introduced to identify the control chart pattern. This method was verified through stochastic simulation. The result demonstrates that the model can cluster each control chart pattern effectively and improve recognition accuracy.