Download Advanced Intelligent Computing: 7th International by Lingzhi Wang, Jiansheng Wu (auth.), De-Shuang Huang, Yong PDF

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

This booklet 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 offered have been conscientiously reviewed and chosen from 832 submissions. The papers are geared up in topical sections on neural networks; computing device studying thought and strategies; fuzzy conception 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 photograph processing; biometrics with functions to person security/forensic sciences; clever image/document retrievals; ordinary language processing and computational linguistics; clever facts fusion and data safety; clever computing in development reputation; clever agent and net purposes; clever computing in scheduling; clever regulate and automation.

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In the experiment phase five companies from different Balkan stock exchanges where selected. Three of them “Komercijalna Banka” (KMB), “Makedonski Neural Network Model for Forecasting Balkan Stock Exchanges 21 Telekom” (MTEL) and “Toplifikacija” (TPLF) are from Macedonian Stock Exchange, “Hrvatski Telekom” (HRT-H-A) from Zagreb Stock Exchange and “Magyar Telekom” (MTELEKOM) from Budapest Stock exchange. These companies were selected because they are one of the most successful in the appropriate stock exchanges and appropriate industries.

4 Training and Testing Strategy The training and testing strategy is probably the most important issue in designing a NN. Choosing the appropriate size of the training set is task that has big influence on the generalization capabilities on the NN and is highly dependent on the nature of the observed time series. Moreover, even if the network can converge, it may have learned some historical patterns that may no longer be effective because the market conditions have already changed too drastically.

Then the HRRPs are made range-profile-aligning. 4 (4) where a(n) is the magnitude of target echo in the n th range cell. After the nonlinear transformation, the HRRPs’ distribution in feature space is better fitted by Gaussiandistribution. Therefore Gaussian classifiers can be used to make target recognition[3]. 2 Feature Extraction Considering the aspect sensitivity of HRRPs, we get range profiles in a small aspect angle to guarantee that the HRRPs remain relatively stable. After Preprocessing, some target features can be extracted.

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