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"Artificial Neural Networks: Architectures and Applications" ed. by Kenji Suzuki

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"Artificial Neural Networks: Architectures and Applications" ed. by Kenji Suzuki

"Artificial Neural Networks: Architectures and Applications" ed. by Kenji Suzuki
InTeOp | 2013 | ISBN: 9535109358 9789535109358 | 264 pages | PDF | 11 MB

The purpose of this book is to provide recent advances of architectures, methodologies, and applications of artificial neural networks. This book will be a fundamental source of recent advances and applications of artificial neural networks. The target audience of this book includes college and graduate students, and engineers in companies.

Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications.
The book consists of two parts: the architecture part covers architectures, design, optimization, and analysis of artificial neural networks; the applications part covers applications of artificial neural networks in a wide range of areas including biomedical, industrial, physics, and financial applications.

Contents
Preface
Section 1 Architecture and Design
1 Improved Kohonen Feature Map Probabilistic Associative Memory Based on Weights Distribution
2 Biologically Plausible Artificial Neural Networks
3 Weight Changes for Learning Mechanisms in Two-Term Back-Propagation Network
4 Robust Design of Artificial Neural Networks Methodology in Neutron Spectrometry
Section 2 Applications
5 Comparison Between an Artificial Neural Network and Logistic Regression in Predicting Long Term Kidney Transplantation Outcome
6 Edge Detection in Biomedical Images Using Self-Organizing Maps
7 MLP and ANFIS Applied to the Prediction of Hole Diameters in the Drilling Process
8 Integrating Modularity and Reconfigurability for Perfect Implementation of Neural Networks
9 Applying Artificial Neural Network Hadron - Hadron Collisions at LHC
10 Applications of Artificial Neural Networks in Chemical Problems
11 Recurrent Neural Network Based Approach for Solving Groundwater Hydrology Problems
12 Use of Artificial Neural Networks to Predict The Business Success or Failure of Start-Up Firms
with TOC BookMarkLinks