Abstract:Effective tool life prediction holds important research value in that it can improve the machining efficiency and ensure the machining accuracy of a workpiece. However, accurate tool life prediction is difficult to achieve as it is influenced by many factors such as tool material, cutting parameters, and machining material. So we propose a method of tool life prediction based on a radial basis function (RBF) neural network optimized by the particle swarm optimization (PSO) algorithm. Firstly, the main parameters of the RBF neural network, namely the center value c, width σ, and connection weight w, are optimized by the PSO algorithm. Then, tool life prediction is carried out, with the factors affecting tool life as input neurons of the PSO-RBF neural network model and tool life as the output neuron. The experimental results show that the proposed method of tool life prediction based on the PSO-RBF neural network is feasible, with an average relative error reduced by 17.14% from that of the standard RBF neural network to 6.16%.

Abstract:Aiming at the inaccuracy of English teaching quality evaluation, a teaching quality evaluation method based on Genetic Algorithm (GA) to optimize RBF neural network is proposed. Firstly, the principal component analysis is used to select the evaluation index of teaching quality, then the RBF neural network teaching evaluation model is designed, and the initial weight of RBF neural network is optimized by GA. The experimental results show that the method can effectively evaluate the quality of English teaching, and has high accuracy and real-time.

Abstract:City gas load forecasting is significant to the operation of city gas networks. In consideration of the periodicity and nonlinearity of gas load data and the shortcomings of a single model, a hybrid model of Echo State Network (ESN) and improved RBF Neural Network (RBFNN) is put forward. First of all, kernel Fisher linear discriminant is utilized for dimension reduction. Secondly, we adopt ESN to do a preliminary prediction. Then, differential evolution integrated with gradient descent by encoding is used to learn and optimize the structure and parameters of RBFNN. Last but not least, the produced result of ESN is the input of RBFNN. It is validated that the proposed model has a higher precision and convergence rate compared with the initial combinational model.

Abstract:To enhance the accuracy of the text classification, a new method based on quantum PSO and RBF neural network is proposed. Firstly, it establishes the key words set to describe the classification of the samples, and uses fuzzy vector space model to build the feature vectors of every kind of sample, then automatically classifies the texts by RBF neural network, optimizes the parameters of RBF neural network by improved quantum PSO to enhance its approximation capability. The new method is proved by the classification of some documents in China periodical document database. The experiment shows that this method makes significant improvements in classification accuracy compared to other methods.

Abstract:This paper introduces a new SAS monitoring system. The system utilizes a self-designed app invoked by mobile phones to collect the data of users snore. Then it transmits the data via home wireless network by means of ftp and finally stores the data in a PC. Moreover, the neural net algorithm and voice-recognition technology have been inserted into core algorithm of the system to identify voice and snore, which can implement the diagnosis of the SAS by combining the analyses of symptoms. The system has a higher disease classification rate than a traditional SAS monitoring system.

Abstract:This paper introduces a hybrid learning algorithm for Radial Basis Function neural network(RBFNN) based on subtractive clustering, K-means clustering and particle swarm optimization algorithm(PSO). The algorithm can be used to determine the number of hidden layer nodes and initial clustering centers of K-means by using subtractive clustering; Then the initial particle swarm of PSO can be formed by K-means clustering algorithm．The basic PSO algorithm are optimized and developed to improving convergence and stability of the algorithm, and finally the improved PSO algorithm is used to train all the parameters of RBFNN. The simulation for IRIS data set classification problem is executed, the experiment results show that the improved hybrid algorithm has higher accuracy and better stability than several other popular methods.

Abstract:Because of the Characteristics of RBF neural network structure is simple, output and initialized weights irrelevant, adaptive, less adjustable parameter etc. This paper proposes using the method of cross validation to find the optimal parameter value of SPREAD, constructs the optimal RBF neural network model and combines the algorithm of MIV to use for variables screening. Through the example test the validity of the model, also make the method has better ability of stability and applied.

Abstract:The neural network is a kind of the commonly used method of data mining, principal component analysis method is a kind of method that analyzes internal relationship between the many variables of the multivariate analysis of statistical. Combined the principal component analysis pretreatment method with neural network, you can analyze the relationship between the original variables, reduce dimensions of the original data and reduce the scale of data. This paper does research on the neural network algorithm and the principal component analysis correlative theory. Based on this, combined with a large number of meteorological data and disease data of Beijing, we proposed an improved method of the data mining which based on principal component analysis and neural network algorithm preprocessing. Through the contrast experiment test, the combinations of the algorithm have a large degree increase in the convergence rate and forecast accuracy property.