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Neural Network PC Tools : A Practical Guide.

By: Eberhart, Russell C.
Contributor(s): Dobbins, Roy W.
Material type: materialTypeLabelBookPublisher: Saint Louis : Elsevier Science & Technology, 2014Copyright date: ©1991Description: 1 online resource (431 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9781483297002.Subject(s): Microcomputers | Neural computers | Neural networks (Neurobiology)Genre/Form: Electronic books.Additional physical formats: Print version:: Neural Network PC Tools : A Practical GuideDDC classification: 006.3 Online resources: Click here to view book
Contents:
Front Cover -- Neural Network PC Tools: A Practical Guide -- Copyright Page -- Table of Contents -- Dedication -- CONTRIBUTGRS -- FOREWORD -- Introduction -- CHAPTER 1. Background and History -- Introduction -- Biological Basis for Neural Network Tools -- Neural Network Development History -- CHAPTER 2. Implementations -- Introduction -- The Back-Propagation Model -- The Self-Organization Model -- CHAPTER 3. Systems Considerations -- Introduction -- Evaluating Problem Categories -- The Big Picture -- Choosing Effective Roles for Neural Networks -- CHAPTER 4. Software Tools -- Introduction -- Implementing Neural Networks on the PC -- Implementation Issues -- CHAPTER 5. Development Environments -- Introduction -- What is a Neural Network Development Environment? -- Introduction to Network Modeling Languages -- Specifying Neural Network Modeis -- A Brief Survey of Neural Network Development Environments -- CaseNet: A Neural Network Development Environment -- CHAPTER 6. Hardware Implementations -- The Transputer -- Using Transputers in Parallel -- Programming the Transputers -- Mini Case Study: Ship Image Recognition -- Summary -- CHAPTER 7. Performance Metrics -- Introduction -- Percent Correct -- Average Sum-Squared Error -- Normalized Error -- Receiver Operating Characteristic Curves -- Recall and Precision -- Other ROC-Related Measures -- Chi-Square Test -- CHAPTER 8. Network Analysis -- Introduction -- Network Analysis -- Relation Factors -- CHAPTER 9. Expert Networks -- Rule-Based Expert Systems -- Expert Networks -- Expert Network Characteristics -- Hybrid Expert Networks -- CHAPTER 10. Case Study I: Detection of Electroencephalogram Spikes -- Introduction -- Goals and Objectives -- Design Process -- System Specifications -- Background -- Data Preprocessing and Categorization -- Test Results.
CHAPTER 11. Case Study II: Radar Signal Processing -- Introduction -- Description of the Radar Facility -- Operation of the System and Data Collection -- Goals and Objectives -- The Design Process -- Results and Discussion -- Conclusions -- CHAPTER 12. Case Study III: Technology in Search of a Buck -- Introduction -- Markets to Watch and Markets to Trade -- Futures Market Forecasting -- Historical Futures Market Data -- Sources of Market Model Data -- Futures Market Model Description -- Why Neural Networks -- Why Excel? -- Current Status, Future Plans and Money Made -- CHAPTER 13. Case Study IV: Optical Character Recognition -- From .PCX to .TXT Viaa Neural Network -- Why OCR Is Such a Bear -- Objects -- Notes and Conclusions -- Acknowledgments -- For more information, consult the following -- CHAPTER 14. Case Study V: Making Music -- Introduction -- Representing Music for Neural Network Tools -- Network Configurations -- Stochasticity, Variability, and Surprise -- Playing Your Music with MIDI -- Now What? -- GLOSSARY -- REFERENCES -- APPENDIX A -- APPENDIX Β -- APPENDIX C -- APPENDIX D -- APPENDIX Ε: Additional Resources -- Introduction -- Organizations and Societies -- Conference and Symposia -- Journals, Magazines, and Newsletters -- Computer Bulletin Boards -- Computer Databases -- Summary -- APPENDIX F -- INDEX.
Summary: This is the first practical guide that enables you to actually work with artificial neural networks on your personal computer. It provides basic information on neural networks, as well as the following special features: source code listings in C**actual case studies in a wide range of applications, including radar signal detection, stock market prediction, musical composition, ship pattern recognition, and biopotential waveform classification**CASE tools for neural networks and hybrid expert system/neural networks**practical hints and suggestions on when and how to use neural network tools to solve real-world problems.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
QA76.5.N42827 1990 (Browse shelf) https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=1877144 Available EBC1877144

Front Cover -- Neural Network PC Tools: A Practical Guide -- Copyright Page -- Table of Contents -- Dedication -- CONTRIBUTGRS -- FOREWORD -- Introduction -- CHAPTER 1. Background and History -- Introduction -- Biological Basis for Neural Network Tools -- Neural Network Development History -- CHAPTER 2. Implementations -- Introduction -- The Back-Propagation Model -- The Self-Organization Model -- CHAPTER 3. Systems Considerations -- Introduction -- Evaluating Problem Categories -- The Big Picture -- Choosing Effective Roles for Neural Networks -- CHAPTER 4. Software Tools -- Introduction -- Implementing Neural Networks on the PC -- Implementation Issues -- CHAPTER 5. Development Environments -- Introduction -- What is a Neural Network Development Environment? -- Introduction to Network Modeling Languages -- Specifying Neural Network Modeis -- A Brief Survey of Neural Network Development Environments -- CaseNet: A Neural Network Development Environment -- CHAPTER 6. Hardware Implementations -- The Transputer -- Using Transputers in Parallel -- Programming the Transputers -- Mini Case Study: Ship Image Recognition -- Summary -- CHAPTER 7. Performance Metrics -- Introduction -- Percent Correct -- Average Sum-Squared Error -- Normalized Error -- Receiver Operating Characteristic Curves -- Recall and Precision -- Other ROC-Related Measures -- Chi-Square Test -- CHAPTER 8. Network Analysis -- Introduction -- Network Analysis -- Relation Factors -- CHAPTER 9. Expert Networks -- Rule-Based Expert Systems -- Expert Networks -- Expert Network Characteristics -- Hybrid Expert Networks -- CHAPTER 10. Case Study I: Detection of Electroencephalogram Spikes -- Introduction -- Goals and Objectives -- Design Process -- System Specifications -- Background -- Data Preprocessing and Categorization -- Test Results.

CHAPTER 11. Case Study II: Radar Signal Processing -- Introduction -- Description of the Radar Facility -- Operation of the System and Data Collection -- Goals and Objectives -- The Design Process -- Results and Discussion -- Conclusions -- CHAPTER 12. Case Study III: Technology in Search of a Buck -- Introduction -- Markets to Watch and Markets to Trade -- Futures Market Forecasting -- Historical Futures Market Data -- Sources of Market Model Data -- Futures Market Model Description -- Why Neural Networks -- Why Excel? -- Current Status, Future Plans and Money Made -- CHAPTER 13. Case Study IV: Optical Character Recognition -- From .PCX to .TXT Viaa Neural Network -- Why OCR Is Such a Bear -- Objects -- Notes and Conclusions -- Acknowledgments -- For more information, consult the following -- CHAPTER 14. Case Study V: Making Music -- Introduction -- Representing Music for Neural Network Tools -- Network Configurations -- Stochasticity, Variability, and Surprise -- Playing Your Music with MIDI -- Now What? -- GLOSSARY -- REFERENCES -- APPENDIX A -- APPENDIX Β -- APPENDIX C -- APPENDIX D -- APPENDIX Ε: Additional Resources -- Introduction -- Organizations and Societies -- Conference and Symposia -- Journals, Magazines, and Newsletters -- Computer Bulletin Boards -- Computer Databases -- Summary -- APPENDIX F -- INDEX.

This is the first practical guide that enables you to actually work with artificial neural networks on your personal computer. It provides basic information on neural networks, as well as the following special features: source code listings in C**actual case studies in a wide range of applications, including radar signal detection, stock market prediction, musical composition, ship pattern recognition, and biopotential waveform classification**CASE tools for neural networks and hybrid expert system/neural networks**practical hints and suggestions on when and how to use neural network tools to solve real-world problems.

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