Prediction Method of Protein Disulfide Bond Based on Pattern Selection

The effect of the different training samples is different for the classifier when pattern recognition system is established. The training samples were selected randomly in the past protein disulfide bond prediction methods, therefore the prediction accuracy of protein contact was reduced. In order to improve the influence of training samples, a prediction method of protein disulfide bond on the basis of pattern selection and Radical Basis Function neural network has been brought forward in this paper. The attributes related with protein disulfide bond are extracted and coded in the method and pattern selection is used to select training samples from coded samples in order to improve the precision of protein disulfide bond prediction. 200 proteins with disulfide bond structure from the PDB database are encoded according to the encoding approach and are taken as models of training samples. Then samples are taken on the pattern selection based on the nearest neighbor algorithm and corresponding prediction models are set by using RBF neural network. The simulation experiment result indicates that this method of pattern selection can improve the prediction accuracy of protein disulfide bond.

A recognition model of survival situations for survivable systems

Due to the lack of pre-recognition and post- prediction in existing survivable systems, a recognition model of survival situations for survivable systems is proposed. First, the survival situation data is clustered into several survival clusters with different service levels based on the Ward method, and then the survival clusters are classified and recognized by means of the error-eliminating decision-making method, which can realize the pre-recognition of the system＇s survival situation. Secondly, the differentiated survival situation data is used to generate stationary predicting sequences. The autoregressive integrated moving average （ARIMA） model is constructed, and the stability, randomness and reversibility index of the model are verified by the auto- correlation function and partial auto-correlation function. Finally, fuzzy particles and the residual correction for the support vector regression （SVR） model are applied to realize the post-prediction of the survival situation. Compared with traditional decision-making methods, the simulation experiments show that the pre-recognition module can not only cluster the survival situation data and identify the service ranks, but can also recognize the illegal users. According to the prediction of abnormal situations numbers and residual correction, the model can effectively realize the post- prediction of survival situations for survivable systems.

A Prediction Method of Protein Disulfide Bond Based on Hybrid Strategy

A prediction method of protein disulfide bond based on support vector machine and sample selection is proposed in this paper. First, the protein sequences selected are en-coded according to a certain encoding, input data for the prediction model of protein disulfide bond is generated;Then sample selection technique is used to select a portion of input data as training samples of support vector machine;finally the prediction model training samples trained is used to predict protein disulfide bond. The result of simulation experiment shows that the prediction model based on support vector ma-chine and sample selection can increase the prediction accuracy of protein disulfide bond.

Classification of Remote Sensing Images Based on Band Selection and Multi-mode Feature Fusion

As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other,the traditional method of single-mode data analysis and processing cannot effectively fuse the data of different modes and express the correlation between different modes.In order to solve this problem,make better fusion of different modal data and the relationship between the said features,this paper proposes a fusion method of multiple modal spectral characteristics and radar remote sensing imageaccording to the spatial dimension in the form of a vector or matrix for effective integration,by training the SVM model.Experimental results show that the method based on band selection and multi-mode feature fusion can effectively improve the robustness of remote sensing image features.Compared with other methods,the fusion method can achieve higher classification accuracy and better classification effect.