Neural Networks in Bioprocessing and Chemical Engineering.

By: Baughman, D. RContributor(s): Liu, Y. AMaterial type: TextTextSeries: eBooks on DemandPublisher: Burlington : Elsevier Science, 2014Description: 1 online resource (509 p.)ISBN: 9781483295657Subject(s): Biotechnological process control | Chemical process control | Neural computersGenre/Form: Electronic books.Additional physical formats: Print version:: Neural Networks in Bioprocessing and Chemical EngineeringDDC classification: 660.6028563 LOC classification: TP248.25.M65 .B38 1996Online resources: Click here to view this ebook.
Contents:
Front Cover; Neural Networks in Bioprocessing and Chemical Engineering; Copyright Page; Table of Contents; Preface; Software Selection and References; Acknowledgments; About the Authors; Chapter 1. Introduction to Neural Networks; 1.1 Introduction; 1.2 Properties of Neural Networks; 1.3 Potential Applications of Neural Networks; 1.4 Reported Commercial and Emerging Applications; 1.5 Chapter Summary; References and Further Reading; Chapter 2. Fundamental and Practical Aspects of Neural Computing; 2.1 Introduction to Neural Computing; 2.2 Fundamentals of Backpropagation Learning
2.2 Fundamentals of Backpropagation Learning2.3 Practical Aspects of Neural Computing; 2.4 Standard Format for Presenting Training Data Filesand Neural Network Specifications; 2.5 Introduction to Special Neural Network Architectures; 2.6 Chapter Summary; Nomenclature; Practice Problems; Appendices; References and Further Reading; Chapter 3. Classification: Fault Diagnosis andFeature Categorization; 3.1 Overview of Classification Neural Networks; 3.2 Radial-Basis-Function Networks; 3.3 Comparison of Classification Neural Networks; 3.4 Classification Neural Networks for Fault Diagnosis
3.5 Classification Networks for Feature Categorization3.6 Chapter Summary; Nomenclature; Practice Problems; References and Further Reading; Chapter 4. Prediction and Optimization; 4.1 Introduction; 4.2 Case Study 1: Neural Networks and Nonlinear RegressionAnalysis; 4.3 Case Study 2: Neural Networks as Soft Sensorsfor Bioprocessing; 4.4 Illustrative Case Study: Neural Networks for ProcessQuality Control and Optimization; 4.5 Chapter Summary; Nomenclature; Practice Problems; References and Further Reading; Chapter 5. Process Forecasting, Modeling,and Control of Time-Dependent Systems
5.1 Introduction5.2 Data Compression and Filtering; 5.3 Recurrent Networks for Process Forecasting; 5.4. Illustrative Case Study: Development of a Time- Dependent Network for Predictive Modeling of a BatchFermentation Process; 5.5 Illustrative Case Study: Tennessee Eastman PlantwideControl Problem; 5.6 Neural Networks for Process Control; 5.7 Chapter Summary; Nomenclature; Practice Problems; Appendices; References and Further Reading; Chapter 6. Development of Expert Networks: A Hybrid System of Expert Systemsand Neural Networks; 6.1 Introduction to Expert Networks
6.2 Illustrative Case Study: Bioseparation of Proteins inAqueous Two-Phase Systems6.3 Chapter Summary; Nomenclature; Practice Problems; References and Further Reading; Appendix. Connections between Neural Networksand Multivariate Statistical Methods: An Overview*; 1. Introduction; 2. A Common Framework for Neural Networks and MultivariateStatistical Methods; 3. Linear Projection-Based Methods; 4. A General Hierarchical Training Methodology; 5. An Example; 6. Summary; Acknowledgment; Nomenclature; Abbreviations; References; Glossary; Data Files; Index
Summary: Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides th
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TP248.25.M65 .B38 1996 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1712396 Available EBL1712396

Front Cover; Neural Networks in Bioprocessing and Chemical Engineering; Copyright Page; Table of Contents; Preface; Software Selection and References; Acknowledgments; About the Authors; Chapter 1. Introduction to Neural Networks; 1.1 Introduction; 1.2 Properties of Neural Networks; 1.3 Potential Applications of Neural Networks; 1.4 Reported Commercial and Emerging Applications; 1.5 Chapter Summary; References and Further Reading; Chapter 2. Fundamental and Practical Aspects of Neural Computing; 2.1 Introduction to Neural Computing; 2.2 Fundamentals of Backpropagation Learning

2.2 Fundamentals of Backpropagation Learning2.3 Practical Aspects of Neural Computing; 2.4 Standard Format for Presenting Training Data Filesand Neural Network Specifications; 2.5 Introduction to Special Neural Network Architectures; 2.6 Chapter Summary; Nomenclature; Practice Problems; Appendices; References and Further Reading; Chapter 3. Classification: Fault Diagnosis andFeature Categorization; 3.1 Overview of Classification Neural Networks; 3.2 Radial-Basis-Function Networks; 3.3 Comparison of Classification Neural Networks; 3.4 Classification Neural Networks for Fault Diagnosis

3.5 Classification Networks for Feature Categorization3.6 Chapter Summary; Nomenclature; Practice Problems; References and Further Reading; Chapter 4. Prediction and Optimization; 4.1 Introduction; 4.2 Case Study 1: Neural Networks and Nonlinear RegressionAnalysis; 4.3 Case Study 2: Neural Networks as Soft Sensorsfor Bioprocessing; 4.4 Illustrative Case Study: Neural Networks for ProcessQuality Control and Optimization; 4.5 Chapter Summary; Nomenclature; Practice Problems; References and Further Reading; Chapter 5. Process Forecasting, Modeling,and Control of Time-Dependent Systems

5.1 Introduction5.2 Data Compression and Filtering; 5.3 Recurrent Networks for Process Forecasting; 5.4. Illustrative Case Study: Development of a Time- Dependent Network for Predictive Modeling of a BatchFermentation Process; 5.5 Illustrative Case Study: Tennessee Eastman PlantwideControl Problem; 5.6 Neural Networks for Process Control; 5.7 Chapter Summary; Nomenclature; Practice Problems; Appendices; References and Further Reading; Chapter 6. Development of Expert Networks: A Hybrid System of Expert Systemsand Neural Networks; 6.1 Introduction to Expert Networks

6.2 Illustrative Case Study: Bioseparation of Proteins inAqueous Two-Phase Systems6.3 Chapter Summary; Nomenclature; Practice Problems; References and Further Reading; Appendix. Connections between Neural Networksand Multivariate Statistical Methods: An Overview*; 1. Introduction; 2. A Common Framework for Neural Networks and MultivariateStatistical Methods; 3. Linear Projection-Based Methods; 4. A General Hierarchical Training Methodology; 5. An Example; 6. Summary; Acknowledgment; Nomenclature; Abbreviations; References; Glossary; Data Files; Index

Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides th

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