Based on our knowledge of the functioning human brain, ANNs (artificial neural networks) serve as a modern paradigm for computing. Drawing on the author's substantial experience, this book provides a comprehensive introduction and practical guide to the development, optimization, and application of ANNs in modern environmental and biological analysis. Presenting basic principles together with simulated biological and environmental data sets and real applications in the field, this volume helps scientists use the power of the ANN model to explain physical concepts and to demonstrate complex natural processes.…mehr
Based on our knowledge of the functioning human brain, ANNs (artificial neural networks) serve as a modern paradigm for computing. Drawing on the author's substantial experience, this book provides a comprehensive introduction and practical guide to the development, optimization, and application of ANNs in modern environmental and biological analysis. Presenting basic principles together with simulated biological and environmental data sets and real applications in the field, this volume helps scientists use the power of the ANN model to explain physical concepts and to demonstrate complex natural processes.
Grady Hanrahan received his Ph.D. in Environmental Analytical Chemistry from the University of Plymouth, UK. With experience in directing undergraduate and graduate research, he has taught in the fields of Analytical Chemistry and Environmental Science at California State University, Los Angeles and California Lutheran University. He has written or co-written numerous peer-reviewed technical papers and is the author and editor of four books detailing the use of modern chemometric and computational modeling techniques to solve complex biological and environmental problems.
Inhaltsangabe
Preface. Introduction to Artificial Neural Networks. Learning Paradigms. Data Normalization, Collection and Input. Performance Criteria. Applications in Biological Analysis. Applications in Environmental Analysis. Appendices. Index.
Preface. Introduction to Artificial Neural Networks. Learning Paradigms. Data Normalization, Collection and Input. Performance Criteria. Applications in Biological Analysis. Applications in Environmental Analysis. Appendices. Index.
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