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Download Advances in Neural Networks – ISNN 2009: 6th International by Minghui Jiang, Yongqing Zhao, Yi Shen (auth.), Wen Yu, Haibo PDF

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 complaints of the sixth foreign Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in may well 2009.

The 409 revised papers awarded have been rigorously reviewed and chosen from a complete of 1.235 submissions. The papers are prepared in 20 topical sections on theoretical research, balance, time-delay neural networks, computing device studying, neural modeling, choice making platforms, fuzzy platforms and fuzzy neural networks, help vector machines and kernel equipment, genetic algorithms, clustering and type, trend reputation, clever regulate, optimization, robotics, picture processing, sign processing, biomedical purposes, fault prognosis, telecommunication, sensor community and transportation structures, in addition to applications.

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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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Additional 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

Sample text

Lemma 2. If λmax (((I − αQ)2 )T (I − αQ)2 < 1, then the neural network (6) has the unique equilibrium point x∗ which satisfies x∗ = PΩ (−α∇f (PΩ (x∗ − α∇f (x∗ ))) + PΩ (x∗ − α∇f (x∗ ))). Proof. Let T (x) = PΩ (−α∇f (PΩ (x − α∇f (x))) + PΩ (x − α∇f (x))). Then T (x) − T (y) ≤ (−α∇f (PΩ (x − α∇f (x))) + PΩ (x − α∇f (x))) − (−α∇f (PΩ (y − α∇f (y))) + PΩ (y − α∇f (y))) ≤ (I − αQ)[(PΩ (x − α∇f (x)) −PΩ (y − α∇f (y)))] ≤ λmax (((I − αQ)2 )T (I − αQ)2 ) x − y . (7) Therefore, the mapping T (x) is contractive in Rn .

The paper is structured as followed. In section 2, related work about RBF Network training is introduced. Section 3 presents RBF Network model and parameter selection problem. Section 4 describes PSO algorithm. In Section 5, a proposed PSO-RBF Network model is given. Sections 6 and Section 7 give the experiments setup, results and validation of the proposed model on various datasets. Finally, the conclusion is given in Section 8. 2 Related Work Although there are many studies in RBF Network training, but research on training of RBF Network with PSO is still fresh.

Finally, CCPSO is applied to the optimal operation of a cascade hydropower station. And satisfied result is obtained. This paper is organized as followed. In section 2, we firstly introduced CA and PSO, then, on based of CA and PSO, CCPSO is proposed. In section 3, we test PSO and CCPSO with numerical experiments, and identified the effectiveness of the proposed algorithm. In section 4, we apply CCPSO to the problem of optimal operation of cascade hydropower stations. Finally, we conclude this paper in Section 5.

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