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

This e-book constitutes the completely refereed post-conference court cases of the seventh foreign convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The ninety four revised complete papers awarded have been rigorously reviewed and chosen from 832 submissions. The papers are prepared in topical sections on neural networks; computing device studying idea and strategies; fuzzy idea and types; fuzzy platforms and tender computing; evolutionary studying & genetic algorithms; swarm intelligence and optimization; clever computing in computing device imaginative and prescient; clever computing in snapshot processing; biometrics with functions to person security/forensic sciences; clever image/document retrievals; ordinary language processing and computational linguistics; clever information fusion and data protection; clever computing in trend acceptance; clever agent and net purposes; clever computing in scheduling; clever regulate and automation.

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

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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.

A detailed presentation of applying NNs for stock market prices forecasting is given in [8]. There is provided theoretical background of modeling NNs and an attempt to build stock buying/selling alert systems is made. Besides the good application of slidingwindow one disadvantage in this work is that NN models are only tested with one company from one stock market. These papers [2, 6, 7, 8] have a big influence on our work, especially on selecting the optimal procedure and parameters for modeling NNs for forecasting tasks.

Then the number of pulses containing target echoes equals to M = θ B θ fr (2) s The received signal is converted to digital samples with the sampling rate N. Suppose the number of the echoes is M, then we get the radar echo matrix Amn. The HRRP of Amn can be got by taking the FFT of the return signal. An Approach for High Resolution Radar Target Recognition A mn ⎡ a 11 ⎢a ⎢ 21 = ⎢ a 31 ⎢ ⎢ # ⎢⎣ a M 1 a 12 a 13 " a 22 a 32 a 23 a 33 " " # # % " aM aM 2 3 a1 N ⎤ a 2 N ⎥⎥ a3N ⎥ ⎥ # ⎥ a MN ⎥⎦ 35 (3) To solve the problem of amplitude sensitivity, the HRRP is normalized to remove the influence of the gain of radar receiver amplifier.

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