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E-grāmata: Artificial Neural Networks in Biological and Environmental Analysis

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(California Lutheran University, Thousand Oaks, USA)
  • Formāts: 214 pages
  • Sērija : Analytical Chemistry
  • Izdošanas datums: 18-Jan-2011
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781040209738
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Artificial Neural Networks in Biological and Environmental AnalysisPalielināt
  • Formāts: 214 pages
  • Sērija : Analytical Chemistry
  • Izdošanas datums: 18-Jan-2011
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781040209738
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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:











A brief history of computational neural network models in relation to brain function Neural network operations, including neuron connectivity and layer arrangement Basic building blocks of model design, selection, and application from a statistical perspective Neurofuzzy systems, neuro-genetic systems, and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes

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.

Recenzijas

"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

Foreword xi
Preface xiii
Acknowledgments xv
The Author xvii
Guest Contributors xix
Glossary of Acronyms xxi
Chapter 1 Introduction
1(16)
1.1 Artificial Intelligence: Competing Approaches or Hybrid Intelligent Systems?
1(2)
1.2 Neural Networks: An Introduction and Brief History
3(8)
1.2.1 The Biological Model
5(1)
1.2.2 The Artificial Neuron Model
6(5)
1.3 Neural Network Application Areas
11(2)
1.4 Concluding Remarks
13(1)
References
13(4)
Chapter 2 Network Architectures
17(20)
2.1 Neural Network Connectivity and Layer Arrangement
17(1)
2.2 Feedforward Neural Networks
17(9)
2.2.1 The Perceptron Revisited
17(6)
2.2.2 Radial Basis Function Neural Networks
23(3)
2.3 Recurrent Neural Networks
26(7)
2.3.1 The Hopfield Network
28(2)
2.3.2 Kohonen's Self-Organizing Map
30(3)
2.4 Concluding Remarks
33(1)
References
33(4)
Chapter 3 Model Design and Selection Considerations
37(28)
3.1 In Search of the Appropriate Model
37(1)
3.2 Data Acquisition
38(1)
3.3 Data Preprocessing and Transformation Processes
39(4)
3.3.1 Handling Missing Values and Outliers
39(1)
3.3.2 Linear Scaling
40(1)
3.3.3 Autoscaling
41(1)
3.3.4 Logarithmic Scaling
41(1)
3.3.5 Principal Component Analysis
41(1)
3.3.6 Wavelet Transform Preprocessing
42(1)
3.4 Feature Selection
43(1)
3.5 Data Subset Selection
44(3)
3.5.1 Data Partitioning
45(1)
3.5.2 Dealing with Limited Data
46(1)
3.6 Neural Network Training
47(9)
3.6.1 Learning Rules
47(2)
3.6.2 Supervised Learning
49(1)
3.6.2.1 The Perceptron Learning Rule
50(1)
3.6.2.2 Gradient Descent and Back-Propagation
50(1)
3.6.2.3 The Delta Learning Rule
51(1)
3.6.2.4 Back-Propagation Learning Algorithm
52(2)
3.6.3 Unsupervised Learning and Self-Organization
54(1)
3.6.4 The Self Organizing Map
54(1)
3.6.5 Bayesian Learning Considerations
55(1)
3.7 Model Selection
56(2)
3.8 Model Validation and Sensitivity Analysis
58(1)
3.9 Concluding Remarks
59(1)
References
59(6)
Chapter 4 Intelligent Neural Network Systems and Evolutionary Learning
65(24)
4.1 Hybrid Neural Systems
65(1)
4.2 An Introduction to Genetic Algorithms
65(8)
4.2.1 Initiation and Encoding
67(1)
4.2.1.1 Binary Encoding
68(1)
4.2.2 Fitness and Objective Function Evaluation
69(1)
4.2.3 Selection
70(1)
4.2.4 Crossover
71(1)
4.2.5 Mutation
72(1)
4.3 An Introduction to Fuzzy Concepts and Fuzzy Inference Systems
73(5)
4.3.1 Fuzzy Sets
73(1)
4.3.2 Fuzzy Inference and Function Approximation
74(3)
4.3.3 Fuzzy Indices and Evaluation of Environmental Conditions
77(1)
4.4 The Neural-Fuzzy Approach
78(3)
4.4.1 Genetic Algorithms in Designing Fuzzy Rule-Based Systems
81(1)
4.5 Hybrid Neural Network-Genetic Algorithm Approach
81(4)
4.6 Concluding Remarks
85(1)
References
86(3)
Chapter 5 Applications in Biological and Biomedical Analysis
89(30)
5.1 Introduction
89(1)
5.2 Applications
89(23)
5.2.1 Enzymatic Activity
94(5)
5.2.2 Quantitative Structure-Activity Relationship (QSAR)
99(9)
5.2.3 Psychological and Physical Treatment of Maladies
108(2)
5.2.4 Prediction of Peptide Separation
110(2)
5.3 Concluding Remarks
112(3)
References
115(4)
Chapter 6 Applications in Environmental Analysis
119(32)
6.1 Introduction
119(1)
6.2 Applications
120(26)
6.2.1 Aquatic Modeling and Watershed Processes
120(8)
6.2.2 Endocrine Disruptors
128(5)
6.2.3 Ecotoxicity and Sediment Quality
133(3)
6.2.4 Modeling Pollution Emission Processes
136(5)
6.2.5 Partition Coefficient Prediction
141(2)
6.2.6 Neural Networks and the Evolution of Environmental Change
143(1)
Kudlak
6.2.6.1 Studies in the Lithosphere
144(1)
6.2.6.2 Studies in the Atmosphere
144(1)
6.2.6.3 Studies in the Hydrosphere
145(1)
6.2.6.4 Studies in the Biosphere
146(1)
6.2.6.5 Environmental Risk Assessment
146(1)
6.3 Concluding Remarks
146(1)
References
147(4)
Appendix I Review of Basic Matrix Notation and Operations 151(4)
Appendix II Cytochrome P450 (CYP450) Isoform Data Set Used in Michielan et al. (2009) 155(24)
Appendix III A 143-Member VOC Data Set and Corresponding Observed and Predicted Values of Air-to-Blood Partition Coefficients 179(4)
Index 183
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.
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