Livingstone D.J. (ed.) Artificial Neural Networks. Methods and Application

Humana Press, 2008. — 332 p.Artificial neural networks (ANN) is the name given to a branch of artificial intelligence (AI) research that aims to simulate intelligent behavior by mimicking the way that biological neural networks work. Most AI methods seek to reproduce human intelligence by imitating “what we do,” ANN seek to reproduce it by imitating “the way that we do it.” The origins of ANN predate computers by some decades, but it was not until computers became generally available that real progress could be made in the development of these methods. There was a slight “glitch” of a decade or so following the publication of a book that heavily criticized the possibility of ANN developing into anything useful; since then, progress has been dramatic and these tools have moved on from being oddities used by specialists to general-purpose algorithms for data analysis and pattern recognition tasks.Artificial Neural Networks in Biology and Chemistry: The Evolution of a New Analytical Tool
Overview of Artificial Neural Networks
Bayesian Regularization of Neural Networks
Kohonen and Counterpropagation Neural Networks Applied for Mapping and Interpretation of IR Spectra
Artificial Neural Network Modeling in Environmental Toxicology
Neural Networks in Analytical Chemistry
Application of Artificial Neural Networks for Decision Support in Medicine
Neural Networks in Building QSAR Models
Peptide Bioinformatics: Peptide Classification Using Peptide Machines
Associative Neural Network
Neural Networks Predict Protein Structure and Function
The Extraction of Information and Knowledge from Trained Neural Networks