Feedforward Neural Network Methodology

• Main Author: Fine, Terrence L. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 1999.
• Series: Information Science and Statistics,
• Subjects: Computer science.; Artificial intelligence.; Physics.; Statistics.; Complexity, Computational.; Computer Science.; Artificial Intelligence (incl. Robotics).; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Mathematical Methods in Physics.; Numerical and Computational Physics.; Complexity.

The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These two factors have cooperated to at last provide systems engineers and statisticians with a working, prac- cal, and successful...

Table of Contents:

• Objectives, Motivation, Background, and Organization
• Perceptions—Networks with a Single Node
• Feedforward Networks I: Generalities and LTU Nodes
• Feedforward Networks II: Real-Valued Nodes
• Algorithms for Designing Feedforward Networks
• Architecture Selection and Penalty Terms
• Generalization and Learning.