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Advances in Neural Network Research and Applications

  • Book
  • © 2010

Overview

Editors:
  1. Zhigang Zeng

    1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

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  2. Jun Wang

    1. Department of Mechanical & Automation Engineering, The Chinese University of Hong Kong, Hong Kong

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    You can also search for this editor in PubMed   Google Scholar

  • State of the art of Neural Network Research applied to Electrical Engineering
  • Presents the edited outcome of the Seventh International Symposium on Neural Networks (ISNN 2010) , Shanghai June 6-9 2010, devoted to electrical engineering appliciitons
  • Written by experts in the field

Part of the book series: Lecture Notes in Electrical Engineering (LNEE, volume 67)

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Table of contents (108 chapters)

  1. Front Matter

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  2. Prediction and Forecasting

    1. A Novel Prediction Mechanism with Modified Data Mining Technique for Call Admission Control in Wireless Cellular Network

      • Chen-Feng Wu, Yu-Teng Chang, Chih-Yao Lo, Han-Sheng Zhuang
      Pages 1-9

    2. The Study of Forecasting Model of Rock Burst for Acoustic Emission Based on BP Neural Network and Catastrophe Theory

      • Yunyun Xu, Dongqiang Xu
      Pages 11-19

    3. Application of Wavelet Neural Network to Prediction of Water Content in Crude Oil

      • Huifen Niu, Cuiling Liu, Jinqi Wang, Xiaowen Sun
      Pages 21-27

    4. Prediction of Urban Heat Island Intensity in Chuxiong City with Backpropagation Neural Network

      • Wujun Xi, Ping He
      Pages 29-36

    5. Research on the Fouling Prediction of Heat Exchanger Based on Wavelet Relevance Vector Machine

      • Lingfang Sun, Rina Saqi, Honggang Xie
      Pages 37-45

    6. Electricity Price Forecasting Using Neural Networks Trained with Entropic Criterion

      • Jianhua Zhang, Jingyue Wang, Rui Wang, Guolian Hou
      Pages 47-53

    7. Least Square Support Vector Machine Ensemble for Daily Rainfall Forecasting Based on Linear and Nonlinear Regression

      • Jiansheng Wu, Mingzhe Liu, Long Jin
      Pages 55-64

    8. Estimating Portfolio Risk Using GARCH-EVT-Copula Model: An Empirical Study on Exchange Rate Market

      • Zongrun Wang, Yanbo Jin, Yanju Zhou
      Pages 65-72

    9. Forecasting Financial Time Series via an Efficient CMAC Neural Network

      • Chi-Jie Lu, Jui-Yu Wu
      Pages 73-82

    10. Forecasting Daily Cash Turnover of Bank with EWMA and SVR

      • Wei-min Ma, Wei Lu
      Pages 83-90

    11. Financial Distress Prediction Model via GreyART Network and Grey Model

      • Ming-Feng Yeh, Chia-Ting Chang, Min-Shyang Leu
      Pages 91-100

    12. Risk Assessment Model Based on Immune Theory

      • Tao Liu, Li Shang, Zhifeng Hu
      Pages 101-108

    13. Short-Term Load Forecasting Based on Bayes and RS

      • Yanmei Li
      Pages 109-116
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Keywords

About this book

This book is a part of the Proceedings of the Seventh International Symposium on Neural Networks (ISNN 2010), held on June 6-9, 2010 in Shanghai, China. Over the past few years, ISNN has matured into a well-established premier international symposium on neural networks and related fields, with a successful sequence of ISNN series in Dalian (2004), Chongqing (2005), Chengdu (2006), Nanjing (2007), Beijing (2008), and Wuhan (2009). Following the tradition of ISNN series, ISNN 2010 provided a high-level international forum for scientists, engineers, and educators to present the state-of-the-art research in neural networks and related fields, and also discuss the major opportunities and challenges of future neural network research. Over the past decades, the neural network community has witnessed significant breakthroughs and developments from all aspects of neural network research, including theoretical foundations, architectures, and network organizations, modeling and simulation, empirical studies, as well as a wide range of applications across different domains. The recent developments of science and technology, including neuroscience, computer science, cognitive science, nano-technologies and engineering design, among others, has provided significant new understandings and technological solutions to move the neural network research toward the development of complex, large scale, and networked brain-like intelligent systems. This long-term goals can only be achieved with the continuous efforts from the community to seriously investigate various issues on neural networks and related topics.

Editors and Affiliations

  • Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhigang Zeng

  • Department of Mechanical & Automation Engineering, The Chinese University of Hong Kong, Hong Kong

    Jun Wang

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