By Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)
The two-volume set LNCS 7367 and 7368 constitutes the refereed complaints of the ninth foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers provided have been conscientiously reviewed and chosen from a number of submissions. The contributions are established in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development attractiveness; imaginative and prescient; photo processing; info processing; neurocontrol; and novel applications.
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Extra info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I
However, the learning speed of SVM is slow and the model parameters are very sensitive to the prediction performance. The least square-support vector machines (LS-SVM) can simplify the quadratic program (QP) problem of the SVM to solve a set of linear equations in the sense of sub optimum. Although the learning speed is improved, the sparse of the SVM is lost, and the sensitivity problem of the models’ learning parameters is not solved. The extreme leaning machines (ELM) obtained better learning speed than SVM by using the minimal norm least square method instead of the standard optimization method.
Performance index of proposed models according to context and cluster T L M Q No. of contexts (p) No. 2 shows the output of local model expressed by linear combination of the partition matrix of context FCM and polynomial function, in which the coefficient of polynomial function is estimated through WLSE. The final output of model becomes the summation of local models. In particular, the major advantage of WLSE is that the performance of testing dataset is more stable than that of model based on the standard LSE.
The LANNIA performance is illustrated in Fig. 2). Twenty trajectories were run in ANNIA at the beginning of each LANNIA cycle. At the ﬁrst cycle, 11 trajectories were identiﬁed as true (Fig. 2(a)). 06. When the factors found were deleted from ANNIA at the end of the ﬁrst LANNIA cycle, at the second cycle, ANNIA found three factors. 015, that is proportional to the number of the found factors. 01. After deleting the factors found at the third cycle, all trajectories at ANNIA happened to be spurious and the procedure was terminated.