Modern Optimization Methods for Science, Engineering and Technology. (Record no. 36802)
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| fixed length control field | 11744nam a22006253i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC31253104 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240724115947.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240724s2020 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780750324045 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780750324052 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC31253104 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL31253104 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1130295074 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA402.5 .M634 2020 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.3 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sinha, G. R. |
| 245 10 - TITLE STATEMENT | |
| Title | Modern Optimization Methods for Science, Engineering and Technology. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Bristol : |
| Name of producer, publisher, distributor, manufacturer | Institute of Physics Publishing, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2020. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (433 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 490 1# - SERIES STATEMENT | |
| Series statement | IOP Ebooks Series |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Intro -- Preface -- Acknowledgements -- Editor biography -- G R Sinha -- List of contributors -- Chapter 1 Introduction and background to optimization theory -- 1.1 Historical development -- 1.1.1 Robustness and optimization -- 1.2 Definition and elements of optimization -- 1.2.1 Design variables and parameters -- 1.2.2 Objectives -- 1.2.3 Constraints and bounds -- 1.3 Optimization problems and methods -- 1.3.1 Workflow of optimization methods -- 1.3.2 Classification of optimization methods -- 1.4 Design and structural optimization methods -- 1.4.1 Structural optimization -- 1.4.2 Design optimization -- 1.5 Optimization for signal processing and control applications -- 1.5.1 Signal processing optimization -- 1.5.2 Communication and control optimization -- 1.6 Design vectors, matrices, vector spaces, geometry and transforms -- 1.6.1 Linear algebra, matrices and design vectors -- 1.6.2 Vector spaces -- 1.6.3 Geometry, transforms, binary and fuzzy logic -- References -- Chapter 2 Linear programming -- 2.1 Introduction -- 2.2 Applicability of LPP -- 2.2.1 The product mix problem -- 2.2.2 Diet problem -- 2.2.3 Transportation problem -- 2.2.4 Portfolio optimization -- 2.3 The simplex method -- 2.4 Artificial variable techniques -- 2.5 Duality -- 2.6 Sensitivity analysis -- 2.7 Network models -- 2.7.1 Shortest path problem -- 2.8 Dual simplex method -- 2.9 Software packages to solve LPP -- Further reading -- Chapter 3 Multivariable optimization methods for risk assessment of the business processes of manufacturing enterprises -- 3.1 Introduction -- 3.2 A mathematical model of a business process -- 3.3 The market and specific risks, the features of their account -- 3.4 Measurement of the risk of using the discount rate, expert assessments and indicators of sensitivity -- 3.5 Conclusion -- References. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Chapter 4 Nonlinear optimization methods-overview and future scope -- 4.1 Introduction -- 4.1.1 Optimization -- 4.1.2 NLP -- 4.1.3 Nonlinear optimization problem and models -- 4.2 Convex analysis -- 4.2.1 Sets and functions -- 4.2.2 Convex cone -- 4.2.3 Concave function -- 4.2.4 Nonlinear optimization: the interior-point approach -- 4.3 Applications of nonlinear optimizations techniques -- 4.3.1 LOQO: an interior-point code for NLP -- 4.3.2 Digital audio filter -- 4.4 Future research scope -- References -- Chapter 5 Implementing the traveling salesman problem using a modified ant colony optimization algorithm -- 5.1 ACO and candidate list -- 5.2 Description of candidate lists -- 5.3 Reasons for the tuning parameter -- 5.4 The improved ACO algorithm -- 5.4.1 Dynamic candidate set based on nearest neighbors -- 5.4.2 Heuristic parameter updating -- 5.5 Improvement strategy -- 5.5.1 2-Opt local search -- 5.6 Procedure of IACO -- 5.7 Flow of IACO -- 5.8 IACO for solving the TSP -- 5.9 Implementing the IACO algorithm -- 5.10 Experiment and performance evaluation -- 5.10.1 Evaluation criteria -- 5.10.2 Path evaluation model -- 5.10.3 Evaluation of solution quality -- 5.11 TSPLIB and experimental results -- 5.11.1 Experiment 1 (analysis of tour length results) -- 5.11.2 Experiment 2 (comparison of convergence speed) -- 5.12 Comparison experiment -- 5.13 Analysis on varying number of ants -- 5.13.1 Analysis of ants starting at different cities versus the same city -- 5.13.2 Analysis on an increasing number of ants versus number of iterations -- 5.14 IACO comparison results -- 5.15 Conclusions -- References -- Chapter 6 Application of a particle swarm optimization technique in a motor imagery classification problem -- 6.1 Introduction -- 6.1.1 Literature review -- 6.1.2 Motivation and requirements -- 6.2 Particle swarm optimization. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 6.2.1 The mathematical model of PSO -- 6.2.2 Constraint-based optimization -- 6.3 Proposed method -- 6.3.1 Materials and methods -- 6.3.2 Classification -- 6.4 Results -- 6.5 Conclusion -- References -- Chapter 7 Multi-criterion and topology optimization using Lie symmetries for differential equations -- 7.1 Introduction -- 7.2 Fundamentals of topological manifolds -- 7.2.1 Analytic manifolds -- 7.2.2 Lie groups and vector fields -- 7.3. Differential equations, groups and the jet space -- 7.3.1 Prolongation of group action and vector fields -- 7.3.2 Total derivatives of vector fields and general prolongation formula -- 7.3.3 Criterion of maximal rank and infinitesimal invariance for differential equations -- 7.3.4 Differential equations and symmetry groups -- 7.3.5 Differential invariants and the group invariant solutions -- 7.4 Classification of the group invariant solutions and optimal solutions -- 7.4.1 Adjoint representation for the cKdV and optimization of the group generators -- 7.4.2 Calculation of the optimal group invariant solutions for the cKdV -- 7.5 Concluding remarks -- References -- Chapter 8 Learning classifier system -- 8.1 Introduction -- 8.2 Background -- 8.3 Classification learner tools -- 8.3.1 MATLAB®: classification learner app -- 8.3.2 BigML® -- 8.3.3 Microsoft® AzureML® -- 8.4 Sample dataset -- 8.4.1 Splitting the dataset -- 8.5 Learning classifier algorithms -- 8.5.1 Logistic regression classifiers -- 8.5.2 Decision tree classifiers -- 8.5.3 Discriminant analysis classifiers -- 8.5.4 Support vector machine classifiers -- 8.5.5 Nearest neighbor classifiers -- 8.5.6 Ensemble classifiers -- 8.6 Performance -- 8.6.1 Confusion matrix -- 8.6.2 Receiver operating characteristic -- 8.6.3 Parallel plot -- 8.7 Conclusion -- Acknowledgments -- References. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Chapter 9 A case study on the implementation of six sigma tools for process improvement -- 9.1 Introduction -- 9.1.1 Generation and cleaning of BF gas -- 9.2 Problem overview -- 9.3 Project phase summaries -- 9.3.1 Definition -- 9.3.2 Measurement -- 9.3.3 Analyze and improvement -- 9.3.4 Control -- 9.4 Conclusion -- 9.4.1 Financial benefits -- 9.4.2 Non-financial benefits -- Chapter 10 Performance evaluation and measures -- 10.1 Performance measurement models -- 10.1.1 Fuzzy sets -- 10.2 AHP and fuzzy AHP -- 10.2.1 Fuzzy AHP -- 10.2.2 Linear programming method -- 10.3 Performance measurement in the production approach -- 10.3.1 Free disposability hull -- 10.4 Data envelopment analysis -- 10.4.1 CCR model -- 10.4.2 BCC model -- 10.4.3 Other models -- 10.5 R as a tool for DEA -- References -- Chapter 11 Evolutionary techniques in the design of PID controllers -- 11.1 PID controller -- 11.1.1 Design procedure -- 11.1.2 Method 1: PID controller design using PSO -- 11.1.3 Method 2: PID controller design using BBBC -- 11.2 FOPID controller -- 11.2.1 Statement of the problem -- 11.2.2 BBBC aided tuning of FOPID controller parameters -- 11.2.3 Illustrative examples -- 11.3 Conclusion -- References -- Chapter 12 A variational approach to substantial efficiency for linear multi-objective optimization problems with implications for market problems -- 12.1 Introduction -- 12.2 Background -- 12.3 A review of substantial efficiency -- 12.4 New results and examples -- 12.5 Conclusion -- References -- Chapter 13 A machine learning approach for engineering optimization tasks -- 13.1 Optimization: classification hierarchy -- 13.2 Optimization problems in machine learning -- 13.3 Optimization in supervised learning -- 13.3.1 Bayesian optimization -- 13.3.2 Bayesian optimization for weight computation: a case study -- 13.3.3 Bayesian optimal classification: a case study. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 13.3.4 Bayesian optimization via binary classification: a case study -- 13.4 Optimization for feature selection -- 13.4.1 Feature extraction using precedence relations: a case study -- 13.4.2 Feature extraction via ensemble pruning: a case study -- 13.4.3 Feature-vector ranking metrics -- References -- Chapter 14 Simulation of the formation process of spatial fine structures in environmental safety management systems and optimization of the parameters of dispersive devices -- 14.1 The use of spatial finely dispersed multiphase structures in ensuring ecological and technogenic safety -- 14.1.1 Analysis of recent research and publications -- 14.1.2 Statement of the problem and its solution -- 14.2 Physical and mathematical simulation of the creation process of spatial finely dispersed structures -- 14.2.1 Gas phase study and mathematical model description -- 14.2.2 Dispersed phase study and mathematical model description -- 14.2.3 Mathematical model of interfacial interaction -- 14.3 Numerical simulation of the formation of spatial dispersed structures and the determination of the most effective ways of supplying fluid to eliminate various hazards -- 14.3.1 Ensuring numerical solution stability, convergence and accuracy -- 14.3.2 Description of the numerical integration method of the dispersed phase equations -- 14.3.3 Results of numerical simulation of a spatial finely dispersed structure creation process which suppresses dust -- 14.3.4 Results of numerical simulation of the spatial finely dispersed structure creation process, which instantly reduces the gas stream temperature -- 14.4 General conclusions -- References -- Chapter 15 Future directions: IoT, robotics and AI based applications -- 15.1 Introduction -- 15.1.1 The impact of AI and robotics in medicine and healthcare -- 15.1.2 Advances in AI technology and their impact on the workforce. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 15.1.3 AI technologies and human intelligence. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book reviews the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner along with their potential applications and implementation strategies. The book will be useful for a wide spectrum of target readers such as research scholars, academics and industry professionals. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Mathematical optimization. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Operations research. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ahmed, Sirajuddin. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chamorshikar, Rajesh. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Choubey, Siddharth. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Choubey, Abha. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Deng, Sien. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Desai, Santosh R. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dewangan, Somesh Kumar. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Gorbunov, Vladimir. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Guggari, Ahankru. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Sinha, G. R. |
| Title | Modern Optimization Methods for Science, Engineering and Technology |
| Place, publisher, and date of publication | Bristol : Institute of Physics Publishing,c2020 |
| International Standard Book Number | 9780750324052 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | IOP Ebooks Series |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=31253104">https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=31253104</a> |
| Public note | Click to View |
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