Improved Nonlinear PCA Based on RBF Networks and Principal Curves

摘要：

Nonlinear PCA based on neural networks (NN) have been widely used in different applications in the past decade. There is a difficulty with the determination of the optimal topology for the networks that are used. Principal curves were introduced to nonlinear PCA to separate the original complex five-layer NN into two three-layer RBF networks and eased the above problem. Using the advantage of Fast Recursive Algorithm, where the number of neurons, the location of centers, and the weights between the hidden layer and the output layer can be identified simultaneously for the RBF networks, the topology problem for the nonlinear PCA based on NN can thus be solved. The simulation result shows that the method is excellent for solving nonlinear principal component problems.

作者: Xueqin Liu Kang Li Marion McAfee Jing Deng

作者单位: School of Electronics, Electrical Engineering and Computer Science, Queens University Belfast.BT9 5 Department of Mechanical and Electronic Engineering, Institute of Technology Sligo, Sligo,Ireland

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 7-15

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）