214 pages | 7 Color Illus. | 55 B/W Illus.
Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and environmental processes.
Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental, technological, and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field, the book stimulates communication and partnership among scientists in fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry.
The book covers:
Scientists deal with very complicated systems, much of the inner workings of which are frequently unknown to researchers. Using only simple, linear mathematical methods, information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed, and ANNs can play a major role. Balancing basic principles and diverse applications, this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.
"…overall it is a concise and readable account of neural networks applied to biological and environmental systems. It combines fundamental, technical and applied aspects and encourages an interdisciplinary approach to extracting information from large and complex datasets."
—Paul Worsfold, University of Plymouth
Glossary of Acronyms
Artificial Intelligence: Competing Approaches or Hybrid Intelligent Systems?
Neural Networks: An Introduction and Brief History
Neural Network Application Areas
Neural Network Connectivity and Layer Arrangement
Feedforward Neural Networks
Recurrent Neural Networks
Model Design and Selection ConsiderationsIn Search of the Appropriate Model
Data Preprocessing and Transformation Processes
Data Subset Selection
Neural Network Training
Model Validation and Sensitivity Analysis
Intelligent Neural Network Systems and Evolutionary LearningHybrid Neural Systems
An Introduction to Genetic Algorithms
An Introduction to Fuzzy Concepts and Fuzzy
The Neural-Fuzzy Approach
Hybrid Neural Network-Genetic Algorithm Approach
Applications in Biological and Biomedical AnalysisIntroduction
Applications in Environmental AnalysisIntroduction
Appendix I: Review of Basic Matrix Notation and Operations
Appendix II: Cytochrome P450 (CYP450) Isoform Data Set Used in Michielan et al (2009)
Appendix III: A 143-Member VOC Data Set and Corresponding Observed and Predicted Values of Air-to-Blood Partition Coefficients