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Artificial neural networks in biological and environmental analysis /
"Drawing on the experience and knowledge of a practicing professional, this book provides a comprehensive introduction and practical guide to the development, optimization, and application of artificial neural networks (ANNs) in modern environmental and biological analysis. Based on our knowled...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
©2011.
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| Colección: | Analytical chemistry series (CRC Press)
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Hanrahan, Grady. | |
| 245 | 1 | 0 | |a Artificial neural networks in biological and environmental analysis / |c Grady Hanrahan. |
| 260 | |a Boca Raton, FL : |b CRC Press, |c ©2011. | ||
| 300 | |a 1 online resource (xxii, 188 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Analytical chemistry series | |
| 520 | |a "Drawing on the experience and knowledge of a practicing professional, this book provides a comprehensive introduction and practical guide to the development, optimization, and application of artificial neural networks (ANNs) in modern environmental and biological analysis. Based on our knowledge of the functioning human brain, ANNs serve as a modern paradigm for computing. Presenting basic principles of ANNs together with simulated biological and environmental data sets and real applications in the field, this volume helps scientists comprehend the power of the ANN model to explain physical concepts and demonstrate complex natural processes"--Provided by publisher | ||
| 520 | |a "The cornerstones of research into prospective tools of artificial intelligence originate from knowledge of the functioning brain. Like most transforming scientific endeavors, this field-- once viewed with speculation and doubt--has had profound impacts in helping investigators elucidate complex biological, chemical, and environmental processes. Such efforts have been catalyzed by the upsurge in computational power and availability, with the co-evolution of software, algorithms, and methodologies contributing significantly to this momentum. Whether or not the computational power of such techniques is sufficient for the design and construction of truly intelligent neural systems is of continued debate. In writing Artificial Neural Networks in Biological and Environmental Analysis, my aim was to provide in-depth and timely perspectives on the fundamental, technological, and applied aspects of computational neural networks. By presenting basic principles of neural networks together with real applications in the field, I seek to stimulate communication and partnership among scientists in the 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 acquiescence of the use of neural network tools in scientific inquiry"--Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g ch. 1 |t Introduction -- |g 1.1. |t Artificial Intelligence: Competing Approaches or Hybrid Intelligent Systems? -- |g 1.2. |t Neural Networks: An Introduction and Brief History -- |g 1.2.1. |t The Biological Model -- |g 1.2.2. |t The Artificial Neuron Model -- |g 1.3. |t Neural Network Application Areas -- |g 1.4. |t Concluding Remarks -- |t References -- |g ch. 2 |t Network Architectures -- |g 2.1. |t Neural Network Connectivity and Layer Arrangement -- |g 2.2. |t Feedforward Neural Networks -- |g 2.2.1. |t The Perceptron Revisited -- |g 2.2.2. |t Radial Basis Function Neural Networks -- |g 2.3. |t Recurrent Neural Networks -- |g 2.3.1. |t The Hopfield Network -- |g 2.3.2. |t Kohonen's Self-Organizing Map -- |g 2.4. |t Concluding Remarks -- |t References -- |g ch. 3 |t Model Design and Selection Considerations -- |g 3.1. |t In Search of the Appropriate Model -- |g 3.2. |t Data Acquisition. |
| 505 | 0 | 0 | |g 3.3. |t Data Preprocessing and Transformation Processes -- |g 3.3.1. |t Handling Missing Values and Outliers -- |g 3.3.2. |t Linear Scaling -- |g 3.3.3. |t Autoscaling -- |g 3.3.4. |t Logarithmic Scaling -- |g 3.3.5. |t Principal Component Analysis -- |g 3.3.6. |t Wavelet Transform Preprocessing -- |g 3.4. |t Feature Selection -- |g 3.5. |t Data Subset Selection -- |g 3.5.1. |t Data Partitioning -- |g 3.5.2. |t Dealing with Limited Data -- |g 3.6. |t Neural Network Training -- |g 3.6.1. |t Learning Rules -- |g 3.6.2. |t Supervised Learning -- |g 3.6.2.1. |t The Perceptron Learning Rule -- |g 3.6.2.2. |t Gradient Descent and Back-Propagation -- |g 3.6.2.3. |t The Delta Learning Rule -- |g 3.6.2.4. |t Back-Propagation Learning Algorithm -- |g 3.6.3. |t Unsupervised Learning and Self-Organization -- |g 3.6.4. |t The Self Organizing Map -- |g 3.6.5. |t Bayesian Learning Considerations -- |g 3.7. |t Model Selection -- |g 3.8. |t Model Validation and Sensitivity Analysis -- |g 3.9. |t Concluding Remarks -- |t References. |
| 505 | 0 | 0 | |g Ch. 4 |t Intelligent Neural Network Systems and Evolutionary Learning -- |g 4.1. |t Hybrid Neural Systems -- |g 4.2. |t An Introduction to Genetic Algorithms -- |g 4.2.1. |t Initiation and Encoding -- |g 4.2.1.1. |t Binary Encoding -- |g 4.2.2. |t Fitness and Objective Function Evaluation -- |g 4.2.3. |t Selection -- |g 4.2.4. |t Crossover -- |g 4.2.5. |t Mutation -- |g 4.3. |t An Introduction to Fuzzy Concepts and Fuzzy Inference Systems -- |g 4.3.1. |t Fuzzy Sets -- |g 4.3.2. |t Fuzzy Inference and Function Approximation -- |g 4.3.3. |t Fuzzy Indices and Evaluation of Environmental Conditions -- |g 4.4. |t The Neural-Fuzzy Approach -- |g 4.4.1. |t Genetic Algorithms in Designing Fuzzy Rule-Based Systems -- |g 4.5. |t Hybrid Neural Network-Genetic Algorithm Approach -- |g 4.6. |t Concluding Remarks -- |t References -- |g ch. 5 |t Applications in Biological and Biomedical Analysis -- |g 5.1. |t Introduction -- |g 5.2. |t Applications -- |g 5.2.1. |t Enzymatic Activity -- |g 5.2.2. |t Quantitative Structure-Activity Relationship (QSAR). |
| 505 | 0 | 0 | |g 5.2.3. |t Psychological and Physical Treatment of Maladies -- |g 5.2.4. |t Prediction of Peptide Separation -- |g 5.3. |t Concluding Remarks -- |t References -- |g ch. 6 |t Applications in Environmental Analysis -- |g 6.1. |t Introduction -- |g 6.2. |t Applications -- |g 6.2.1. |t Aquatic Modeling and Watershed Processes -- |g 6.2.2. |t Endocrine Disruptors -- |g 6.2.3. |t Ecotoxicity and Sediment Quality -- |g 6.2.4. |t Modeling Pollution Emission Processes -- |g 6.2.5. |t Partition Coefficient Prediction -- |g 6.2.6. |t Neural Networks and the Evolution of Environmental Change / |r Kudlak -- |g 6.2.6.1. |t Studies in the Lithosphere -- |g 6.2.6.2. |t Studies in the Atmosphere -- |g 6.2.6.3. |t Studies in the Hydrosphere -- |g 6.2.6.4. |t Studies in the Biosphere -- |g 6.2.6.5. |t Environmental Risk Assessment -- |g 6.3. |t Concluding Remarks -- |t References. |
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Artificial intelligence |x Biological applications. | |
| 650 | 0 | |a Biology |x Data processing. | |
| 650 | 0 | |a Environmental engineering |x Data processing. | |
| 650 | 0 | |a Neural networks (Computer science) |x Scientific applications. | |
| 650 | 6 | |a Intelligence artificielle |x Applications biologiques. | |
| 650 | 6 | |a Biologie |x Informatique. | |
| 650 | 6 | |a Technique de l'environnement |x Informatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) |x Applications scientifiques. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Chemistry |x General. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Chemistry |x Analytic. |2 bisacsh | |
| 650 | 7 | |a NATURE |x Reference. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Life Sciences |x Biology. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Life Sciences |x General. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |x Biological applications |2 fast | |
| 650 | 7 | |a Biology |x Data processing |2 fast | |
| 650 | 7 | |a Environmental engineering |x Data processing |2 fast | |
| 758 | |i has work: |a Artificial neural networks in biological and environmental analysis (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGMgGvcdqDKYQH7GVfyRPP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Hanrahan, Grady. |t Artificial neural networks in biological and environmental analysis. |d Boca Raton, FL : CRC Press, ©2011 |z 9781439812587 |w (DLC) 2010038461 |w (OCoLC)667210573 |
| 830 | 0 | |a Analytical chemistry series (CRC Press) | |
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