By Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, Hongwei Li (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)
This booklet and its sister quantity gather refereed papers offered on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. construction at the luck of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of renowned and fine quality meetings on neural computation and its functions. ISNN goals at supplying a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to provide and talk about the most recent progresses in neural networks, and purposes in different components. these days, the sphere of neural networks has been fostered a ways past the normal man made neural networks. This 12 months, ISNN 2010 bought 591 submissions from greater than forty nations and areas. in keeping with rigorous experiences, a hundred and seventy papers have been chosen for ebook within the court cases. The papers gathered within the court cases conceal a large spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and purposes of neural networks. we've equipped the papers into volumes according to their themes. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent subject matters: neurophysiological starting place, concept and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 issues: SVM and kernel tools, imaginative and prescient and photograph, information mining and textual content research, BCI and mind imaging, and applications.
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Extra info for Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II
1) can be written as: f (x ) = ∑ (a N i =1 i ) − a i* · K ( x i , x ) + b (8) K(xi , x)=< φ(xi), φ(x)> is the so-called kernel function . Any symmetric positive semi-definite function that satisfies Mercer’s Conditions  can be used as a kernel function. Our work is based on the RBF kernel . 3 ACO-SVR Model This study proposed a new method, ACO-SVR, which optimized all SVR’s parameters simultaneously through ACO evolutionary process. Then, the acquired parameters were used to construct optimized SVR model.
When each subset has one sample, the polynomial kernel function based on the data vector equals to twice of the polynomial-matrix one based on the autocorrelation matrix. That means the degree d is the twice of the degree D. Proof. When each subset contains one sample, the autocorrelation matrix Σi = xi xT i . Using the polynomial-matrix kernel function, it follows: T T D κ(Σi , Σj ) = ||Σi . ∗ Σi ||D B = ||xi xi . ∗ xj xj ||B ⎛ x2i1 xi1 xi2 ⎜ xi2 xi1 x2i2 ⎜ = || ⎜ . ⎝ .. xin xi1 xin xi2 ⎞ ⎛ 2 xj1 xj1 xj2 .
Jianfu Yang, Hongying Sun, Fengjian Yang, Wei Li, and Dongqing Wu Discrete Time Nonlinear Identiﬁcation via Recurrent High Order Neural Networks for a Three Phase Induction Motor . . . . . . . . . Alma Y. Alanis, Edgar N. Sanchez, Alexander G. Loukianov, and Marco A. Perez-Cisneros 711 719 Stability Analysis for Stochastic BAM Neural Networks with Distributed Time Delays . . . . . . . . . . . . . . . . . . . . . Guanjun Wang 727 Dissipativity in Mean Square of Non-autonomous Impulsive Stochastic Neural Networks with Delays .