In order to effectively utilize the network teaching resources, a teaching resource classification method based on the improved KNN (K-Nearest Neighbor) algorithm was proposed. Taking the text class primary and secondary school teaching resources as the research object, combined with the domain characteristics, the KNN algorithm was improved. By measuring the sample space density, the text of the high-density area was found. Different clipping methods were proposed for both intra-class and inter-class regions. The problem of cropping in the space of multiple class boundaries was considered. Results showed that the method ensured uniform distribution of samples and reduced the time of classification. Therefore, under the Weka platform, the improved KNN algorithm is effective.

In wireless body area networks WBANs, node trajectory prediction is the basis of routing, power controlling, lifetime prolonging and connectivity maintaining. The grey model is adopted and improved in node trajectory prediction in this paper. A novel variable weight buffer for the grey model is introduced to solve the problem of inconsistency between qualitative analysis and quantitative calculation. The relationship between variable weight and its regulation degree is then discussed. Furthermore, an input data feature-based self-adaptive strategy is proposed to address the restrictions of nodes' calculation capacity and limited storage space. This feature allows the user to determine the variable weight automatically. The proposed algorithm holds the capability to identify high-quality forecasting over the database of MSR Daily Activity 3D, which is captured by Microsoft Research and contains 16 daily activities, and it outperforms existing methods significantly in terms of effectiveness and adaptability.

Specific to the lack of effective domain division method and much first-order fuzzy relationship, this paper proposes a second-order Markov model based fuzzy time series prediction method. It uses fuzzy C-means clustering to obtain the membership of elements in the time series. It introduces the transition matrix in second-order Markov model to represent fuzzy relations. It updates traditional representation and calculation of fuzzy relations. It forecasts the element’s membership in fuzzy clusters and defuzzifies the membership using the center-of-gravity method. It applies the model to the performance predic-tion of China Mobile 3G, and the accuracy is improved when compared to the traditional fuzzy time series prediction method.

Aiming to the need of proactive monitoring and performance prediction in 3G networks,it proposes a prediction method of the Gaussian regression model based on the median filter,integrates the Gaussian regression model with the median filtering method,pretreats the sample data with median filtering,and then the processed data is done to the Gaussian regression prediction,the prediction results are as the prediction curve of the active alarm mechanism.Simulation results show that compared to other prediction algorithms,the Gaussian process based on median filtering predicts more effectively and generates more accurate prediction curves.It provides a theoretical basis for proactive monitoring in 3G and above network to determine an effective threshold.

Specific to the need of performance prediction in communication networks,a connection rate prediction method based on fuzzy Auto-Regressive(AR) model was proposed and improved,and the fuzzy AR model based on adaptive fitting degree threshold was studied.The median filtering method was applied to pre-process the data of fuzzy AR model.On this basis,for the uncertain thresholds of some applications,the fitting degree threshold formula was added to the prediction model to make it adaptive.The simulation results show that the predistion method based on fuzzy AR model can be used to predict the connection rate with a higher fitting degree.