Optimization and decision making (Record no. 35254)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06901cam a22003018i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | CUTN |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20210806151347.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190820s2020 nju bf 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789811205668 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Cancelled/invalid ISBN | 9789811205675 |
| 041 ## - LANGUAGE CODE | |
| Language | English |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | pcc |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | MAR |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Marwala, Tshilidzi, |
| 245 10 - TITLE STATEMENT | |
| Title | Optimization and decision making |
| Statement of responsibility, etc | Tshilidzi Marwala, Collins Achepsah Leke, University of Johannesburg, South Africa. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | pages cm |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Title | Contents<br/>Chapter 1: Introduction<br/>1.1 Introduction<br/>1.2 A Brief Description of This Book<br/>1.3 Conclusion<br/>References<br/>Chapter 2: Classical Optimization<br/>2.1 Introduction<br/>2.2 Optimization<br/>2.3 Nelder–Mead Simplex Method<br/>2.4 Quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) Algorithm<br/>2.5 Conjugate Gradient (CG) Method<br/>2.6 Parameter Estimation of a Simple Beam<br/>2.7 Parameter Estimation of an Unsymmetrical H-shaped Structure<br/>2.8 Conclusion<br/>References<br/>Chapter 3: Genetic Algorithm<br/>3.1 Genetic Algorithm<br/>3.1.1 Initialization<br/>3.1.2 Crossover<br/>3.1.3 Mutation<br/>3.1.4 Selection<br/>3.1.5 Termination<br/>3.2 Remanufacturing<br/>3.3 Unsymmetrical H-shaped Structure<br/>3.4 Conclusion<br/>References<br/>Chapter 4: Particle Swarm Optimization<br/>4.1 Introduction<br/>4.2 Particle Swarm Optimization<br/>4.3 Genetic Algorithm Versus PSO<br/>4.4 Operating System Scheduler Tuning<br/>4.5 Unsymmetrical H-shaped Structure<br/>4.6 Missing Data Estimation<br/>4.7 Conclusion<br/>References<br/>Chapter 5: Simulated Annealing<br/>5.1 Introduction<br/>5.2 Simulated Annealing (SA)<br/>5.2.1 Simulated annealing parameters<br/>5.2.2 Transition probabilities<br/>5.2.3 Monte Carlo method<br/>5.2.4 Markov Chain Monte Carlo (MCMC)<br/>5.2.5 Acceptance probability function: Metropolis algorithm<br/>5.2.6 Cooling schedule<br/>5.3 Particle Swarm Optimization Versus SA<br/>5.4 Solving Sudoku<br/>5.5 Unsymmetrical H-shaped Structure<br/>5.6 Conclusion<br/>References<br/>Chapter 6: Response Surface Method<br/>6.1 Introduction<br/>6.2 Response Surface Method (RSM)<br/>6.3 Neural Networks<br/>6.3.1 Multi-layer perceptron (MLP)<br/>6.3.2 Training the multi-layer perceptron<br/>6.3.3 Backpropagation method<br/>6.3.4 Scaled conjugate gradient method<br/>6.4 Evolutionary Optimization<br/>6.5 Estimating Parameters of a Beam<br/>6.6 Estimating Parameters of an Unsymmetrical H-shaped Structure<br/>6.7 Conclusion<br/>References<br/>Chapter 7: Ant Colony Optimization<br/>7.1 Introduction<br/>7.2 Ant Colony Optimization<br/>7.3 Missing Data Estimation Using ACO and Neural Networks<br/>7.4 Condition Monitoring of Bushings<br/>7.4.1 Dissolved gas analysis (DGA) experiment<br/>7.4.2 Data preprocessing<br/>7.5 Conclusion<br/>References<br/>Chapter 8: Bat and Firefly Algorithms<br/>8.1 Introduction<br/>8.2 Bat Algorithm<br/>8.3 Firefly Algorithm<br/>8.4 Deep Learning<br/>8.5 Restricted Boltzmann Machine (RBM)<br/>8.6 Contrastive Divergence (CD)<br/>8.7 Missing Data Estimation Using BA, FA and Neural Networks<br/>8.8 Conclusion<br/>References<br/>Chapter 9: Artificial Immune System<br/>9.1 Introduction<br/>9.2 Artificial Immune System<br/>9.2.1 Innate immunity<br/>9.2.2 Acquired immunity<br/>9.2.3 Self-tolerant trait<br/>9.2.4 Immune system memory<br/>9.3 Artificial Immune System Basic Concepts<br/>9.3.1 Initialization/Encoding<br/>9.3.2 Similarity or affinity measure<br/>9.3.3 Somatic hypermutation<br/>9.4 The Concept of the Negative Selection Algorithm<br/>9.4.1 Implementation of algorithm<br/>9.4.2 Training<br/>9.4.3 Testing<br/>9.5 Missing Data Estimation Using AIS and Neural Networks<br/>9.6 Conclusion<br/>References<br/>Chapter 10: Invasive Weed Optimization and Cuckoo Search Algorithms<br/>10.1 Introduction<br/>10.2 Invasive Weed Optimization<br/>10.3 Cuckoo Search Algorithm<br/>10.4 Deep Autoassociative Neural Network<br/>10.5 Missing Data Estimation Using IWO, CS and Neural Networks<br/>10.6 Conclusion<br/>References<br/>Chapter 11: Decision Trees and Random Forests<br/>11.1 Introduction<br/>11.2 Decision Trees<br/>11.3 Random Forests<br/>11.3.1 Common variables for random forest<br/>11.4 Missing Data Estimation Using Decision Trees and AANN-GA<br/>11.5 Missing Data Estimation Using Random Forests<br/>11.5.1 Missing data estimation using random forests hybrids<br/>11.6 Conclusion<br/>References<br/>Chapter 12: Hybrid Methods<br/>12.1 Introduction<br/>12.2 Finite Element Model Updating<br/>12.3 Nelder–Mead Simplex Method<br/>12.4 Structural Mechanics<br/>12.5 Hybrid PSO and the Nelder–Mead Simplex<br/>12.6 Finite Element Updating<br/>12.7 Conclusion<br/>References<br/>Chapter 13: Economic Modeling<br/>13.1 Introduction<br/>13.2 Economic Concepts<br/>13.3 Pitfalls on Economic Data Modeling<br/>13.4 Data Handling<br/>13.5 Neural Networks<br/>13.6 Support Vector Machine<br/>13.7 Rough Sets<br/>13.8 Autoassociative Networks<br/>13.9 Incremental Learning<br/>13.10 Conclusion<br/>References<br/>Chapter 14: Condition Monitoring<br/>14.1 Introduction<br/>14.2 Condition Monitoring Framework<br/>14.3 Stages of Condition Monitoring<br/>14.4 Data Representation for Condition Monitoring<br/>14.4.1 Time domain<br/>14.4.2 Modal domain<br/>14.4.3 Frequency domain<br/>14.4.4 Time–frequency<br/>14.5 Correlation-Based Condition Monitoring<br/>14.6 Finite Element-Based Condition Monitoring<br/>14.7 Artificial Intelligence Methods<br/>14.7.1 Multi-layer perceptrons<br/>14.7.2 Hidden Markov models (HMMs)<br/>14.7.3 Fuzzy logic<br/>14.7.4 Rough sets<br/>14.7.5 Support vector machines (SVMs)<br/>14.8 Conclusion<br/>References<br/>Chapter 15: Rational Decision-Making<br/>15.1 Introduction<br/>15.2 Can Rationality be Measured?<br/>15.2.1 Information<br/>15.2.2 Model: Biological and physical brain<br/>15.2.3 What about the uncertainty principle?<br/>15.2.4 Optimization<br/>15.2.5 Classification of rationality<br/>15.2.6 Rationality quantification<br/>15.3 Can AI Machines Be Rational?<br/>15.3.1 What did Herbert Simon say?<br/>15.3.2 What happens when we replace humans with machines?<br/>15.3.3 Digital and quantum computing<br/>15.3.4 All models are wrong<br/>15.3.5 Can machines be rational?<br/>15.4 Are Markets Rational?<br/>15.5 Unbounded Rational Decision-Making<br/>15.6 Bounded Rational Decision-Making<br/>15.7 Flexibly Bounded Rational Decision-Making<br/>15.8 Condition Monitoring<br/>15.9 Modeling HIV<br/>15.10 Conclusion<br/>References<br/>Chapter 16: Concluding Remarks<br/>16.1 Summary of the Book<br/>References<br/>Index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts"-- |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Leke, Collins Achepsah, |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Reference Books |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Dates associated with a name | 1971- |
| Relator term | author. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 1911 |
| 490 0# - SERIES STATEMENT | |
| Series statement | Handbook of machine learning; |
| Volume number/sequential designation | volume 2 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Relator term | author. |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
| a | 0 |
| b | ibc |
| c | orignew |
| d | 1 |
| e | ecip |
| f | 20 |
| g | y-gencatlg |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Location | Shelving location | Date of Cataloging | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Non-fiction | CUTN Central Library | CUTN Central Library | Reference | 06/07/2021 | 006.31 MAR | 44011 | 06/07/2021 | 06/07/2021 | Reference Books |