Time series clustering analysis based on wavelet and process neural networks

Transcription

1 ELECTRI C MACHINES AND CONTROL Vol. 15 No. 12 Dec UCI TP A X Time series clustering analysis based on wavelet and process neural networks GE Li 1 2 YIN Gui-sheng 1 1. College of Computer Science and Technology Harbin Engineering University Harbin China 2. School of Computer and Information Engineering Harbin Commerce University Harbin China Abstract For time series clustering problem a method based on wavelet and improved self-organization process neural networks PNN was proposed. First original time series data was decomposed by wavelet. Under the principle of reserving clustering characteristics the signal was reconstructed. And then reconstructed signal fitted into time-varying functions was used as PNN's input. Self-organization PNN was trained by improved competition algorithm. Making use of time-varying input characteristic of PNN the timing signal characteristics processed by wavelet has been considered adequately in clustering analysis. Network extracts implicit process mode characteristics of function to self organize. The improved competition learning algorithm was given. Finally clustering result of UCI datasets shows that the proposed approach has an improvement in clustering accuracy network runtime and convergence speed at the same time shows good performance in clustering accuracy and clustering speed can be applied to timing clustering effectively. Key words time series clustering analysis self-organization process neural networks wavelet GC05A RFQXG

2 系统输出 y 1 y 2 y 1 y m 竞争层 w ij(t) 输入层 X 1(t) X 2(t) X 1(t) X n(t) 系统输入 1 Fig. 1 Topology structure of self-organization process neural networks UCI university of california irvine Synthetic Control Chart 1 Mallat 1 d j = n L w l a il - p j il 1 i = 1 l = 1 Mallat d j j a il i = 1 2 n l = 1 2 L p j il i = 1 2 n l = 1 2 L j w l l = 1 2 L n L j w ij t i = 1 2 n j = 1 2 m 1 i j X t = x 1 t x 2 t x n t y j j = 1 2 A m j 2 A B s j = 槡 1 - d 2 j 2 ε 1

3 80 15 A d A B = n L w l a il - b il 3 i = 1 l = 1 d A B A B 3 a il b il A B w l l = 1 2 L Db3 1 5 Haar ε Normal 5 j 10 p l ij = p l ij + Δp l ij 4 } Δp l ij = η a k il - p l ij 6 Increasing%trend a k il k p l ij Δp l Upward%shift Downward%shift ij j η 2 l = 1 2 L 6 ε 2 max Δp l ij Δp l Fig. 2 Typical table of all kinds of control chart ij signals after reconstruction 5 max Δp l ij ε ~ UCI Synthetic Control Chart η = /0. 8 a current /a m 5 a current a m Synthetic Control Chart ε ε w l = l = max Δp l ij ε % Cyclic Decreasing%trend

4 Table 1 Result comparison based on different initial value /% /s Table 2 2 Comparison of different clustering method result /% /s Fig. 3 Comparison of runtime C1 16 C2 16 C3 16 C4 16 C5 16 C Table 3 3 Clustering accuracy C % C % C % C % C % C % Synthetic Control Charts 10 3 本文算法比较算法 5 J Time/s Datasets HOLGER KANTZ THOMAS SCHREIBER. Nonlinear time series analysis M. Cambridge Cambridge University Press CHUI C K. An Introduction to Wavelets M. New York Academic Press Inc DAUBECHIES I. Ten lectures on Wavelets M. Vermont Capital City Press HERNADEZ E WEISS G. A first Course on Wavelets M. New York CRC Press MWEDM ZHANG Zhihang CUI Hai DING Haijuan et al. The reference plane by wavelets for 3D roughness evaluation of microwire electri-

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