By Jiacai Fu, Thi Tra Giang Dang, Minh Nhut Nguyen, Daming Shi (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)
The 3 quantity set LNCS 5551/5552/5553 constitutes the refereed lawsuits of the sixth foreign Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in may perhaps 2009.
The 409 revised papers awarded have been conscientiously 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, laptop studying, neural modeling, determination making structures, fuzzy structures and fuzzy neural networks, aid vector machines and kernel tools, genetic algorithms, clustering and class, development attractiveness, clever keep an eye on, optimization, robotics, picture processing, sign processing, biomedical functions, fault prognosis, telecommunication, sensor community and transportation structures, in addition to applications.
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Additional 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
Repeat the fifth to seventh steps till the stopping criterion is satisfied and then return the gbest and its objective value as the optimal solution and the optimal value. 5 One Numerical Example Suppose that a ﬁrm intents to open at most two plants and two DCs for producing and distributing products. A decision maker should make a choice from the given arrangement with 3 candidate plants and 3 candidate DCs. There are two raw material suppliers, three customer zones in this problem. The objective of the ﬁrm is to minimize the total costs of the ﬁxed cost and the expected cost.
J; k = 1, . . , K, where ⎧ Q(y, ξ) = min i,j,k ξijk (γ)yijk + r,v,j srvj xrvj ⎪ ⎪ x,qim ⎪ ⎪ ⎪ + ⎪ i,j (pij k yijk ) + i,k,m ζikm (γ)zikm + i,m him qim ⎪ ⎪ ⎪ J ⎪ subject to: j=1 xrvj ≤ arv , r = 1, . . , R, v = 1, . . , V ⎪ ⎪ ⎪ V ⎪ ⎨ nri Ii=1 K k=1 yijk ≤ v=1 xrvj , j = 1, . . , J K k=1 yijk ≤ bij uj , i = 1, . . , I, j = 1, . . , J ⎪ ⎪ M J ⎪ ⎪ ⎪ m=1 zikm ≤ j=1 yijk , i = 1, . . , I, k = 1, . . , K ⎪ ⎪ M ⎪ ⎪ ⎪ m=1 zikm ≤ ωk cik , i = 1, . . , I, k = 1, . . , K ⎪ K ⎪ ⎪ ⎪ k=1 zikm + qim ≥ ηim , i = 1, .
It is important to emphasize that proposed bargaining method is carried out under incomplete information, and agents’ information about own parameters are considered private information. The design can discourage counter-speculation and effectively control fraud and misrepresentation in a certain extent. We hope the method we proposed can be applied in real-world situations. However, this needs to more improvement in theory and practice. Especially, when the issues considered are too many, the implementation of software agent will become more complex.