As an extension of artificial intelligence research, artificial neural networks (ANN) aim to simulate intelligent behavior by mimicking the way that biological neural networks function. In Artificial Neural Networks, an international panel of experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. In the tradition of the highly successful Methods in Molecular BiologyT series, this volume exhibits clear, easy-to-use information with many step-by-step laboratory protocols.
Comprehensive and state-of-the-art, Artificial Neural Networks is an excellent guide to this accelerating technological field of study.
Reihe
Auflage
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Professional/practitioner
Illustrationen
74
74 s/w Abbildungen
X, 254 p. 74 illus.
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 15 mm
Gewicht
ISBN-13
978-1-61737-738-9 (9781617377389)
DOI
10.1007/978-1-60327-101-1
Schweitzer Klassifikation
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.
