By Chen-Feng Wu, Yu-Teng Chang, Chih-Yao Lo, Han-Sheng Zhuang (auth.), Zhigang Zeng, Jun Wang (eds.)
This publication is part of the lawsuits of the 7th overseas Symposium on Neural Networks (ISNN 2010), hung on June 6-9, 2010 in Shanghai, China. ISNN 2010 got a variety of submissions from approximately hundreds of thousands of authors in approximately forty nations and areas throughout six continents . in keeping with the rigorous peer-reviews by way of this system committee participants and the reviewers, 108 top quality papers have been chosen for guides in Lecture Notes in electric Engineering (LNEE) lawsuits. those papers hide all significant issues of the engineering designs and purposes of neural community learn. as well as the contributed papers, the ISNN 2010 technical application integrated 4 plenary speeches through Andrzej Cichocki (RIKEN mind technological know-how Institute, Japan), Chin-Teng Lin (National Chiao Tung collage, Taiwan), DeLiang Wang (Ohio kingdom college, USA), Gary G. Yen (Oklahoma nation college, USA).
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Fig. 2. An example of updating the pattern list The process of updating the pattern list is illustrated in Fig. 2. At time t+1, the actual time series data X(t+1) that is with former pattern [1, 2, 4, -3] is received and transferred into G(t+1) by pre-processing process. If the actual value of G(t+1) is -2, 1, or 6, the count value of the existing pattern list will increase, and the corresponding confident level will be risen at next prediction. A new pattern will be added to the pattern list, if G(t+1) is not an expected value, such as 8.
IEEE/ACM Transactions on Networking 5(1), 1–12 (1997) 15. : Integration of pricing with call admission control to meet QoS requirements in cellular networks. IEEE Transactions on Parallel and Distributed System 19(9), 898–910 (2002) 16. : Local predictive resource reservation for handoff in multimedia wireless IP networks. IEEE Journal on selected areas in Communications 19(10), 1931–1941 (2001) 17. : A clustering prediction scheme for wireless cellular network. In: Proc. of the 2005 International Symposium on Collaborative Technologies and Systems, pp.
The third case is applied to test a suddenly continuous scenario as shown in Fig. 5. The front part of this time series is the same as the first test case except at 00:09 the utilization increases 10% suddenly and lasts in the following time series. The identical pattern can be detected but the prediction value that is derived from the periodic pattern causes one miss. Whatever, the prediction module can restore the accuracy immediately. In the fourth case, the time series prediction sub-module identifies successfully no existence of periodic pattern found.