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Artificial Neural Network Modeling of Water and Wastewater Treatment Processes.

By: Contributor(s): Material type: TextTextSeries: Computer Science, Technology and ApplicationsPublisher: Hauppauge : Nova Science Publishers, Incorporated, 2011Copyright date: ©2011Edition: 1st edDescription: 1 online resource (116 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781612094618
Subject(s): Genre/Form: Additional physical formats: Print version:: Artificial Neural Network Modeling of Water and Wastewater Treatment ProcessesDDC classification:
  • 628.1620113
LOC classification:
  • TD745 -- .K43 2011eb
Online resources:
Contents:
Intro -- ARTIFICIAL NEURAL NETWORK MODELING OF WATER AND WASTEWATER TREATMENT PROCESSES -- ARTIFICIAL NEURAL NETWORK MODELING OF WATER AND WASTEWATER TREATMENT PROCESSES -- CONTENTS -- PREFACE -- Chapter 1 INTRODUCTION -- Chapter 2 TOPOLOGY OF ARTIFICIAL NEURAL NETWORKS -- 2.1. TRANSFER FUNCTIONS -- 2.2. LEARNING PROCESS -- 2.2.1. Supervised Learning -- 2.2.2. Reinforcement Learning -- 2.2.3. Unsupervised Learning -- 2.3. TRAINING ALGORITHMS -- 2.3.1. Back Propagation Algorithm -- 2.3.1.1. Conjugate Gradient Algorithm -- 2.3.1.2. Scaled Conjugate Gradient Algorithm -- 2.3.2. Quasi-Newton Algorithms -- 2.3.3. Levenberg-Marquardt Algorithm -- Chapter 3 TRAINING, VALIDATION AND TEST OF A NEURAL NETWORK -- 3.1. TEST OF THE FITTED MODEL -- 3.2. RELATIVE IMPORTANCE OF INPUT VARIABLES -- 3.3. IMPROVING GENERALIZATION -- 3.3.1. Regularization -- 3.3.2. Early Stopping -- Chapter 4 APPLICATIONS OF ARTIFICIAL NEURAL NETWORK MODELING -- Chapter 5 ANN MODELING OF ADSORPTION PROCESSES -- Chapter 6 ANN MODELING OF BIOLOGICAL TREATMENT PROCESSES -- Chapter 7 ANN MODELING OF ELECTROCHEMICAL TREATMENT PROCESSES -- Chapter 8 ANN MODELING OF PHOTOCATALYTIC PROCESSES -- Chapter 9 ANN MODELING OF PHOTOOXIDATIVE PROCESSES -- 9.1. FENTON AND PHOTO-FENTON PROCESSES -- 9.2. UV/H2O2, OZONATION AND CHLORINATION PROCESSES -- CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- INDEX.
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Intro -- ARTIFICIAL NEURAL NETWORK MODELING OF WATER AND WASTEWATER TREATMENT PROCESSES -- ARTIFICIAL NEURAL NETWORK MODELING OF WATER AND WASTEWATER TREATMENT PROCESSES -- CONTENTS -- PREFACE -- Chapter 1 INTRODUCTION -- Chapter 2 TOPOLOGY OF ARTIFICIAL NEURAL NETWORKS -- 2.1. TRANSFER FUNCTIONS -- 2.2. LEARNING PROCESS -- 2.2.1. Supervised Learning -- 2.2.2. Reinforcement Learning -- 2.2.3. Unsupervised Learning -- 2.3. TRAINING ALGORITHMS -- 2.3.1. Back Propagation Algorithm -- 2.3.1.1. Conjugate Gradient Algorithm -- 2.3.1.2. Scaled Conjugate Gradient Algorithm -- 2.3.2. Quasi-Newton Algorithms -- 2.3.3. Levenberg-Marquardt Algorithm -- Chapter 3 TRAINING, VALIDATION AND TEST OF A NEURAL NETWORK -- 3.1. TEST OF THE FITTED MODEL -- 3.2. RELATIVE IMPORTANCE OF INPUT VARIABLES -- 3.3. IMPROVING GENERALIZATION -- 3.3.1. Regularization -- 3.3.2. Early Stopping -- Chapter 4 APPLICATIONS OF ARTIFICIAL NEURAL NETWORK MODELING -- Chapter 5 ANN MODELING OF ADSORPTION PROCESSES -- Chapter 6 ANN MODELING OF BIOLOGICAL TREATMENT PROCESSES -- Chapter 7 ANN MODELING OF ELECTROCHEMICAL TREATMENT PROCESSES -- Chapter 8 ANN MODELING OF PHOTOCATALYTIC PROCESSES -- Chapter 9 ANN MODELING OF PHOTOOXIDATIVE PROCESSES -- 9.1. FENTON AND PHOTO-FENTON PROCESSES -- 9.2. UV/H2O2, OZONATION AND CHLORINATION PROCESSES -- CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- INDEX.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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