Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window | Zendy

Yunpeng Fan | Zendy; Zhang We | Zendy; Yingwei Zhang | Zendy

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Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window

Author(s) -

Yunpeng Fan,

Zhang We,

Yingwei Zhang

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/546138

Subject(s) - kernel principal component analysis , principal component analysis , nonlinear system , window (computing) , algorithm , computer science , kernel (algebra) , process (computing) , pattern recognition (psychology) , artificial intelligence , mathematics , kernel method , support vector machine , physics , quantum mechanics , combinatorics , operating system

A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted principal component analysis (EWPCA) algorithm respectively. The new algorithm prejudges the new available sample with MKPCA method to decide whether the model is updated. Then update the KPCA model using EWKPCA method. And also extend MPCA and EWPCA from linear data space to nonlinear data space effectively. Monitoring experiment is performed using the proposed algorithm. The simulation results show that the proposed method is effective.

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