Get Advances in Neural Networks – ISNN 2009: 6th International PDF

By Jiacai Fu, Thi Tra Giang Dang, Minh Nhut Nguyen, Daming Shi (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)

ISBN-10: 3642015093

ISBN-13: 9783642015090

The 3 quantity set LNCS 5551/5552/5553 constitutes the refereed lawsuits of the sixth overseas Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in could 2009.

The 409 revised papers offered have been rigorously reviewed and chosen from a complete of 1.235 submissions. The papers are geared up in 20 topical sections on theoretical research, balance, time-delay neural networks, desktop studying, neural modeling, determination making platforms, fuzzy platforms and fuzzy neural networks, help vector machines and kernel equipment, genetic algorithms, clustering and class, development reputation, clever keep watch over, optimization, robotics, photograph processing, sign processing, biomedical functions, fault prognosis, telecommunication, sensor community and transportation platforms, in addition to applications.

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Read Online or Download Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part II PDF

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Extra info for Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part II

Example text

1 Integral Satisfaction Measures in Multi-issue Negotiation For convenience, we assume the preferences of agents can be characterized by quadratic utility functions. Quadratic utility function is one type of nonlinear utility functions that satisfies the assumption that agent’s preference is strictly quasiconcave. e. ¦ ZB 1. Let μ (i) denotes the satisfaction measure of issue i of the 1didn B buyer, μ B (i) ∈ [0 ,1] , the integral satisfaction measure of the buyer is characterized by the following formula: Automated Sealed-Bid Negotiation Model for Multi-issue Based on Fuzzy Method 9 P˄x) Fig.

I, k = 1, . . , K ⎪ K ⎪ ⎪ ⎪ k=1 zikm + qim ≥ ηim , i = 1, . . , I, m = 1, . . , M ⎩ xrvj , yijk , zikm , qim ≥ 0, f or all i, v, j, k, r, m. (2) The goal of the first stage is to decide which plant or DC to be opened. This is called the strategic level supply chain planning in the literature [17]. The corresponding tactical level supply chain is composed of deciding quantities of raw materials purchasing and products distributing. The objective of the model (1) is to minimize the total costs of the first stage costs and the second stage costs.

Zheng value computation will involve multiple integrals and this is impossible. Second, even if the expected value Eξ [Q(y, ξ)] can be exactly computed, optimization of this function presents significant difficulty since it’s can not be expressed in a closed analytical form of y. To overcome the difficulties, we apply approximation method [18] and an NN to approximate the recourse function Eξ [Q(y, ξ)] since a trained NN has the ability to approximate integrable functions. 3 Approximating Two-Stage Supply Chain Problem Suppose that ξ = (ξ1 , ξ2 , · · · , ξr )T is a continuous vector in the supply chain r problem (2) with a support Ξ = i=1 [ai , bi ] ⊂ r .

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Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part II by Jiacai Fu, Thi Tra Giang Dang, Minh Nhut Nguyen, Daming Shi (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)


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