By Minghui Jiang, Yongqing Zhao, Yi Shen (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)
The 3 quantity set LNCS 5551/5552/5553 constitutes the refereed court cases of the sixth overseas Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in could 2009.
The 409 revised papers awarded have been rigorously reviewed and chosen from a complete of 1.235 submissions. The papers are equipped in 20 topical sections on theoretical research, balance, time-delay neural networks, computer studying, neural modeling, determination making platforms, fuzzy platforms and fuzzy neural networks, aid vector machines and kernel tools, genetic algorithms, clustering and category, trend reputation, clever keep an eye on, optimization, robotics, photograph processing, sign processing, biomedical purposes, fault analysis, telecommunication, sensor community and transportation structures, in addition to applications.
Read or Download Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part III PDF
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Extra resources for Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part III
Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections of weights between the hidden layer and the output layer. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network.
The number of basis functions controls the complexity and the generalization ability of RBF Networks. RBF Networks with too few basis functions cannot fit the training data adequately due to limited flexibility. On the other hand, those with too many basis functions yield poor generalization abilities since they are too flexible and fit the noise in the training data. The methods mentioned above require designers to fix the structure of networks in advance according to prior knowledge. However it is difficult for designers to achieve optimal architecture.
Zhao, and Y. Shen 3 Exponential Stability Now, we can show the existence and unique of solution of the neural network (6). Lemma 1. The neural network (6) have only one continuous and unique solution with initial point x(t0 ). Proof. Set F (x) = −x + PΩ (−α∇f (PΩ (x − α∇f (x))) + PΩ (x − α∇f (x))). By the mean-value theorem and the invariance of the norm for the projection operator on closed convex set Ω, we have F (x) − F (y) ≤ y − x + PΩ (−α∇f (PΩ (x − α∇f (x))) + PΩ (x − α∇f (x))) −PΩ (−α∇f (PΩ (y − α∇f (y))) + PΩ (y − α∇f (y))) ≤ y − x + (I − sup α∇2 f (x + t(x − y)))(PΩ (x − α∇f (x)) 0≤t≤1 −PΩ (y − α∇f (y))) ≤ y − x + (I − αQ)2 (x − y)) ≤ [1 + λmax (((I − αQ)2 )T (I − αQ)2 )] y − x = L x−y where L = 1 + λmax (((I − αQ)2 )T (I − αQ)2 .