Artificial Neural Networks

1. Front Matter

   Pages i-xv

   PDF

2. Introduction

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 1-8

3. Neural Network Architectures and Learning Schemes

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 9-85

4. ELEANNE: Efficient LEarning Algorithms for Neural NEtworks

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 87-139

5. Fast Learning Algorithms for Neural Networks

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 141-193

6. ALADIN: Algorithms for Learning and Architecture DetermINation

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 195-218

7. Performance Evaluation of Single-layered Neural Networks

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 219-257

8. High-order Neural Networks and Networks with Composite Key Patterns

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 259-298

9. Applications of Neural Networks: A Case Study

   • N. B. Karayiannis, A. N. Venetsanopoulos

   Pages 299-315

10. Applications of Neural Networks: A Review

    • N. B. Karayiannis, A. N. Venetsanopoulos

    Pages 317-369

11. Future Trends and Directions

    • N. B. Karayiannis, A. N. Venetsanopoulos

    Pages 371-373

12. Back Matter

    Pages 375-440

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1.1 Overview We are living in a decade recently declared as the "Decade of the Brain". Neuroscientists may soon manage to work out a functional map of the brain, thanks to technologies that open windows on the mind. With the average human brain consisting of 15 billion neurons, roughly equal to the number of stars in our milky way, each receiving signals through as many as 10,000 synapses, it is quite a view. "The brain is the last and greatest biological frontier", says James Weston codiscoverer of DNA, considered to be the most complex piece of biological machinery on earth. After many years of research by neuroanatomists and neurophys­ iologists, the overall organization of the brain is well understood, but many of its detailed neural mechanisms remain to be decoded. In order to understand the functioning of the brain, neurobiologists have taken a bottom-up approach of studying the stimulus-response characteristics of single neurons and networks of neurons, while psy­ chologists have taken a top-down approach of studying brain func­ tions from the cognitive and behavioral level. While these two ap­ proaches are gradually converging, it is generally accepted that it may take another fifty years before we achieve a solid microscopic, intermediate, and macroscopic understanding of brain.

• algorithms
• convergence
• high-order neural network
• learning algorithm
• learning scheme
• neural network architecture
• neural networks
• training

• University of Houston, USA

  N. B. Karayiannis

• University of Toronto, Canada

  A. N. Venetsanopoulos

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