The Cook Projection Index Estimation Using the Wavelet Kernel Function

2004 ◽  
pp. 822-827
Author(s):  
Wei Lin ◽  
Tian Zheng ◽  
Fan He ◽  
Xian-bin Wen
Keyword(s):  
Kernel Function ◽  
Projection Index ◽  
Wavelet Kernel Function ◽  
Index Estimation

Research on Nonlinear Modeling Method of Support Vector Machine with Wavelet Derivation Kernel Function

2014 ◽  
Vol 687-691 ◽  
pp. 1408-1411
Author(s):  
Ping An Wang ◽  
Xu Sheng Gan ◽  
Wen Ming Gao
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Nonlinear Modeling ◽  
Nonlinear Function ◽  
Modeling Method ◽  
Good Effect ◽  
Support Vector ◽  
Satisfactory Approximation ◽  
Wavelet Kernel Function ◽  
Nonlinear Function Approximation

The model capability of Support Vector Machine (SVM) relies on the selection of kernel function. To obtain a better application modeling of SVM, the wavelet kernel function that satisfies Merce condition is introduced to use the kernel function of SVM, achieving a good effect. In the paper, on the basis of wavelet kernel function, a wavelet derivation kernel function is proposed in the application of SVM for higher accuracy. An actual example on nonlinear function approximation shows that SVM regression model has a satisfactory approximation effect, and also support an effective nonlinear modeling method.

Prediction of Networks Security Situation Based on Wavelet Kernel Function Network

2011 ◽  
Vol 127 ◽  
pp. 53-58
Author(s):  
Jian Qiong Xiao
Keyword(s):  
Kernel Function ◽  
Optimal Solution ◽  
Support Vector ◽  
Situation Models ◽  
Convex Programming Problem ◽  
Value Prediction ◽  
The Neural Network ◽  
Vector Machines ◽  
Wavelet Kernel Function ◽  
Function Network

Based on the foundation of prediction of networks security situation models, this article proposed a method about applying wavelet kernel function network to prediction of networks security situation. Wavelet kernel function network combined with the neural network and the support vector machines merits, which avoid support vector machine (SVM) solving binding second convex programming problem, network scale doesn't happen dimension disasters problem because kernel function is introduced, and its solution is the global optimal solution, so the situation prediction is more accurate. The experiment tests indicated that this method can accurately acquire the situation value prediction results, it has the good situation prediction potency, which provided one new key for prediction of networks security situation.

Anomaly Intrusion Detection Based on Support Vector Machine with Mexico Hat Wavelet Kernel Function

2014 ◽  
Vol 687-691 ◽  
pp. 3897-3900 ◽  
Author(s):  
Ping An Wang ◽  
Xu Sheng Gan ◽  
Deng Kai Yao
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Kernel Function ◽  
Great Influence ◽  
Model Performance ◽  
Necessary Condition ◽  
Support Vector ◽  
Rbf Kernel ◽  
Wavelet Kernel Function ◽  
Anomaly Intrusion Detection

The selection of kernel function in Support Vector Machine (SVM) has a great influence on the model performance. In the paper, Mexico hat wavelet kernel is introduced to employ the kernel function of SVM, and theoretically it has be prove that, Mexico hat wavelet kernel satisfies the Merce condition, that is the necessary condition as the kernel function of SVM. Simulation on the anomaly detection shows that the capability of SVM based on Mexico hat wavelet kernel is better than that of SVM based on RBF kernel with a satisfactory result for anomaly intrusion detection.

scholarly journals A Novel Fractional-Order PID Controller for Integrated Pressurized Water Reactor Based on Wavelet Kernel Neural Network Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yu-xin Zhao ◽  
Xue Du ◽  
Geng-lei Xia
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Kernel Function ◽  
Control Strategy ◽  
Pressurized Water Reactor ◽  
Pressurized Water ◽  
Water Reactor ◽  
Complex Control System ◽  
Wavelet Kernel Function ◽  
Fractional Order Pid

This paper presents a novel wavelet kernel neural network (WKNN) with wavelet kernel function. It is applicable in online learning with adaptive parameters and is applied on parameters tuning of fractional-order PID (FOPID) controller, which could handle time delay problem of the complex control system. Combining the wavelet function and the kernel function, the wavelet kernel function is adopted and validated the availability for neural network. Compared to the conservative wavelet neural network, the most innovative character of the WKNN is its rapid convergence and high precision in parameters updating process. Furthermore, the integrated pressurized water reactor (IPWR) system is established by RELAP5, and a novel control strategy combining WKNN and fuzzy logic rule is proposed for shortening controlling time and utilizing the experiential knowledge sufficiently. Finally, experiment results verify that the control strategy and controller proposed have the practicability and reliability in actual complicated system.

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