Read e-book online Advanced Intelligent Computing: 7th International PDF

By Lingzhi Wang, Jiansheng Wu (auth.), De-Shuang Huang, Yong Gan, Vitoantonio Bevilacqua, Juan Carlos Figueroa (eds.)

ISBN-10: 364224727X

ISBN-13: 9783642247279

ISBN-10: 3642247288

ISBN-13: 9783642247286

This e-book constitutes the completely refereed post-conference complaints of the seventh foreign convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The ninety four revised complete papers offered have been conscientiously reviewed and chosen from 832 submissions. The papers are equipped in topical sections on neural networks; computer studying conception and strategies; fuzzy conception and types; fuzzy structures and delicate computing; evolutionary studying & genetic algorithms; swarm intelligence and optimization; clever computing in laptop imaginative and prescient; clever computing in photo processing; biometrics with purposes to person security/forensic sciences; clever image/document retrievals; typical language processing and computational linguistics; clever facts fusion and data safety; clever computing in trend attractiveness; clever agent and net purposes; clever computing in scheduling; clever keep an eye on and automation.

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Read or Download Advanced Intelligent Computing: 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011. Revised Selected Papers PDF

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Additional resources for Advanced Intelligent Computing: 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011. Revised Selected Papers

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N − 1; j = 2, . . , N ), Mi (i = 1, . . ) satisfying (3) and the LMIs: ⎞ ⎛ h h h S1,1 S1,2 · · · S1,N h h ⎟ ⎜ ∗ S2,2 · · · S2,N ⎟ ⎜ (11) Sh = ShT = ⎜ . . .. ⎟ ≥ 0, ⎝ .. . ⎠ h ∗ ∗ · · · SN,N ⎞ ⎛ τ τ τ · · · S1,N S1,1 S1,2 τ τ ⎟ ⎜ ∗ S2,2 · · · S2,N ⎟ ⎜ Sτ = SτT = ⎜ . ≥ 0, (12) . . . .. ⎟ ⎠ ⎝ .. τ ∗ ∗ · · · SN,N Ψ= where ⎡ Ψ11 (1, 1) Ω1,2 Ω1,3 ⎢ ∗ Ω2,2 Ω2,3 ⎢ ⎢ ∗ ∗ Ω3,3 ⎢ ⎢ ∗ ∗ ∗ ⎢ ⎢ ∗ ∗ ∗ =⎢ ⎢ ∗ ∗ ∗ ⎢ ⎢ ∗ ∗ ∗ ⎢ ⎢ ∗ ∗ ∗ ⎢ ⎣ ∗ ∗ ∗ ∗ ∗ ∗ Ω1,4 Ω2,4 Ω3,4 Ω4,4 ∗ ∗ ∗ ∗ ∗ ∗ Ψ11 Ψ12 ∗ Ψ22 < 0, Ω1,5 Ω1,6 Ω2,5 Ω2,6 Ω3,5 Ω3,6 Ω4,5 Ω4,6 (5, 5) Ω5,6 ∗ Ω6,6 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ Ω1,7 Ω2,7 Ω3,7 Ω4,7 Ω5,7 Ω6,7 Ω7,7 ∗ ∗ ∗ (13) Ω1,8 Ω2,8 Ω3,8 Ω4,8 Ω5,8 Ω6,8 0 Ω8,8 ∗ ∗ ⎤ (1, 9) (1, 10) Ω2,9 Ω2,10 ⎥ ⎥ Ω3,9 Ω3,10 ⎥ ⎥ ⎥ 0 0 ⎥ Ω5,9 Ω5,10 ⎥ ⎥, Ω6,9 Ω6,10 ⎥ ⎥ ⎥ 0 0 ⎥ ⎥ 0 0 ⎥ ⎦ (9, 9) 0 ∗ (10, 10) 14 D.

Satisfying the following LMIs ⎞ ⎛ P11 + Lττ + Lhh P12 − Lττ P13 − Lhh P14 ⎜ ∗ P22 + Lττ P23 P24 ⎟ ⎟ > 0, ⎜ (3) Lh ⎝ ∗ ∗ P33 + h P34 ⎠ ∗ ∗ ∗ P44 ⎛ Sτ = SτT = τ τ S1,1 S1,2 τ ∗ S2,2 > 0, (4) Sh = ShT = h h S1,1 S1,2 h ∗ S2,2 > 0, (5) Ω1,1 Ω1,2 Ω1,3 ⎜ ∗ Ω2,2 Ω2,3 ⎜ ⎜ ∗ ∗ Ω3,3 ⎜ ⎜ ∗ ∗ ∗ ⎜ ⎜ ∗ ∗ ∗ Ω=⎜ ⎜ ∗ ∗ ∗ ⎜ ⎜ ∗ ∗ ∗ ⎜ ⎜ ∗ ∗ ∗ ⎜ ⎝ ∗ ∗ ∗ ∗ ∗ ∗ Ω1,4 Ω2,4 Ω3,4 Ω4,4 ∗ ∗ ∗ ∗ ∗ ∗ Ω1,5 Ω2,5 Ω3,5 Ω4,5 Ω5,5 ∗ ∗ ∗ ∗ ∗ Ω1,6 Ω2,6 Ω3,6 Ω4,6 Ω5,6 Ω6,6 ∗ ∗ ∗ ∗ Ω1,7 Ω2,7 Ω3,7 Ω4,7 Ω5,7 Ω6,7 Ω7,7 ∗ ∗ ∗ Ω1,8 Ω2,8 Ω3,8 Ω4,8 Ω5,8 Ω6,8 0 Ω8,8 ∗ ∗ Ω1,9 Ω2,9 Ω3,9 0 Ω5,9 Ω6,9 0 0 Ω9,9 ∗ ⎞ Ω1,10 0 ⎟ ⎟ Ω3,10 ⎟ ⎟ 0 ⎟ ⎟ Ω5,10 ⎟ ⎟ < 0, Ω6,10 ⎟ ⎟ 0 ⎟ ⎟ 0 ⎟ ⎟ 0 ⎠ Ω10,10 (6) where T τ h Ω1,1 = P14 + P14 + Qτ + Qh + Qr − Rττ − Rhh − Rrr + S1,1 + S1,1 2Wτ 2Wh T T − τ − h + A M1 + M1 A + rU, T T + Rττ + AT M2T , Ω1,3 = P34 + Rhh + AT M3T + M1 B, Ω1,2 = P24 T T Ω1,4 = P44 + A M4 + M1 D, Ω1,5 = P11 − M1 + AT M5T , T T Ω1,6 = P12 + M1 C + A M6 , Ω1,7 = P13 + AT M7T , Rr T τ T T τ Ω1,8 = −P14 + r + AT M8 , Ω1,9 = S1,2 + 2W τ + A M9 , 2W h T τ Ω1,10 = S1,2 + h h + AT M10 , Ω2,2 = −Qτ − Rττ − S2,2 , T Ω2,3 = M2 B, Ω2,4 = M2 D, Ω2,5 = P12 − M2 , Ω2,6 = P22 + M2 C, Ω2,7 = P23 , Ω2,8 = −P24 , τ T h ) , Ω3,3 = − Rhh − S2,2 − Qh + M3 B + B T M3T , Ω2,9 = (−S1,2 T Ω3,5 = P13 − M3 + B T M5T , Ω3,4 = M3 D + B T M4T , T T T Ω3,6 = P23 + M3 C + B M6 , Ω3,7 = P33 + B T M7T , T T h T T Ω3,8 = −P34 + B M8 , Ω3,9 = B T M9T , Ω3,10 = (−S1,2 ) + B T M10 , U T T T T T Ω4,5 = P14 − M4 + D M5 , Ω4,4 = − r + M4 D + D M4 , T T Ω4,6 = P24 + M4 C, Ω4,7 = P34 , Ω4,8 = −P44 , τ Ω5,5 = hRh + τ Rτ + rRr + 2 Wτ + h2 Wh + Lτ + Lh − M5 − M5T , Ω5,6 = M5 C + M6T , Ω5,7 = −M7T , Ω5,8 = −M8T , Ω5,9 = −M9T , T T T Ω6,6 = −Lτ + M6 C + C M6 , Ω6,7 = C T M7T , Ω5,10 = −M10 , T T T T T T Ω6,9 = C M9 , Ω6,10 = C M10 , Ω7,7 = −Lh , Ω6,8 = C M8 , τ τ h h − S1,1 − τ2 Wτ , Ω10,10 = S2,2 − S1,1 − h2 Wh .

According to [2] main unsolved mysteries in forecasting time series data with NNs are: how NNs model autocorrelated time series data, how to systematically build a NN based model for forecasting tasks, what is the best training method for forecasting tasks and what are the best pre- and postprocessing techniques. Most significant recommendations of NN based financial time series forecasting are given in [6]. They propose and give detailed explanation of almost unified procedure in modeling NNs for forecasting financial time series.

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Advanced Intelligent Computing: 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011. Revised Selected Papers by Lingzhi Wang, Jiansheng Wu (auth.), De-Shuang Huang, Yong Gan, Vitoantonio Bevilacqua, Juan Carlos Figueroa (eds.)


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