Advanced Intelligent Computing Theories and Applications. by Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang,

By Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang, Laurent Heutte, Marco Loog (eds.)

The foreign convention on clever Computing (ICIC) was once shaped to supply an annual discussion board devoted to the rising and not easy themes in synthetic intelligence, computer studying, bioinformatics, and computational biology, and so forth. It goals to convey - gether researchers and practitioners from either academia and to percentage principles, difficulties and strategies with regards to the multifaceted points of clever computing. ICIC 2007, held in Qingdao, China, August 21-24, 2007, constituted the 3rd - ternational convention on clever Computing. It equipped upon the luck of ICIC 2006 and ICIC 2005 held in Kunming and Hefei, China, 2006 and 2005, respectively. This 12 months, the convention targeted commonly at the theories and methodologies in addition to the rising functions of clever computing. Its goal used to be to unify the image of up to date clever computing innovations as an vital idea that highlights the traits in complicated computational intelligence and bridges theoretical learn with purposes. for this reason, the subject for this convention was once “Advanced clever Computing expertise and Applications”. Papers concentrating on this topic have been solicited, addressing theories, methodologies, and purposes in technological know-how and technology.

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Extra info for Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings

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X=0 The ∞-norm of a matrix A = (aij )n×n ∈ IRn×n is n ||A||∞ = max { 1≤i≤n |aij |} . j=1 Locally Asymptotical Behaviors of Delayed BAM Neural Networks 23 For a matrix B = (bik ) ∈ IRn×m (not square matrix), we define generalized norm of matrix B as following: m |B|∞ = max { 1≤i≤n |bik |} . k=1 In the following, we give two properties of matrix’s eigenvalues. Lemma 1. For any eigenvalue λ and any norm || · || of a matrix A ∈ IRn×n , the following inequality holds: |λ| ≤ ||A|| . Proof. Because λ is an eigenvalue of the matrix A, there exits x0 ∈ IRn , x0 = 0 such that Ax0 = λx0 , and |Ax0 | = |λx0 |, this implies that |λ| = |Ax| |Ax0 | ≤ sup = ||A|| .

Salehi, R. Azizi, and A. Salehi Fig. 6. Cumulative oil production of testing wells A, B and C Fig. 7. Productivity map of sector field, lighter areas identify the suitable location for drilling new wells and are hence low productivity regions. The productivity map shown in Figure 7 allows us to place the new well at a location that yields the highest recovery. By using productivity map is selected location of new well for testing recovery. Figure 8 compares production profile of field sector between two different conditions, with field development scheme and a production scenario without new well.

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