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Diversity and non-integer differentiation for system dynamics / / Alain Oustaloup ; series editor Bernard Dubuisson
Diversity and non-integer differentiation for system dynamics / / Alain Oustaloup ; series editor Bernard Dubuisson
Autore Oustaloup Alain
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (383 p.)
Disciplina 003.85
Collana Control, Systems and Industrial Engineering Series
Soggetto topico Dynamics - Mathematical models
System analysis - Mathematical models
ISBN 1-118-76082-4
1-118-76086-7
1-118-76092-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page ; Copyright; Contents; Acknowledgments; Preface; Introduction; Chapter 1: From Diversity to Unexpected Dynamic Performances; 1.1. Introduction; 1.2. An issue raising a technological bottle-neck; 1.3. An aim liable to answer to the issue; 1.4. A strategy idea liable to reach the aim; 1.4.1. Why diversity?; 1.4.2. What does diversity imply?; 1.5. On the strategy itself; 1.5.1. The study object; 1.5.2. A pore: its model and its technological equivalent; 1.5.2.1. The model; 1.5.2.2. The technological equivalent; 1.5.3. Case of identical pores; 1.5.4. Case of different pores
1.5.4.1. On differences coming from regional heritage1.5.4.1.1 Differences of technological origin; 1.5.4.1.2. A difference of natural origin; 1.5.4.1.3. How is difference expressed?; 1.5.4.2. Transposition to the study object; 1.6. From physics to mathematics; 1.6.1. An unusual model of the porous face; 1.6.1.1. A smoothing remarkable of simplicity: the one of crenels; 1.6.1.2. A non-integer derivative as a smoothing result; 1.6.1.3. An original heuristic verification of differentiation non-integer order; 1.6.2. A just as unusual model governing water relaxation
1.7.2.1. Taking into account the past1.7.2.2. Memory notion; 1.7.2.3. A diversion through an aspect of human memory; 1.7.2.3.1. The serial position effect; 1.7.2.3.2. A model of the primacy effect; 1.8. On the nature of diversity; 1.8.1. An action level to be defined; 1.8.2. One or several forms of diversity?; 1.8.2.1. Forms based on the invariance of the elements; 1.8.2.2. A singular form based on the time variability of an element; 1.9. From the porous dyke to the CRONE suspension; 1.10. Conclusion; 1.11. Bibliography; Chapter 2: Damping Robustness; 2.1. Introduction
2.2. From ladder network to a non-integer derivative as a water-dyke interface model2.2.1. On the admittance factorizing; 2.2.2. On the asymptotic diagrams at stake; 2.2.3. On the asymptotic diagram exploiting; 2.2.3.1. Step smoothing; 2.2.3.2. Crenel smoothing; 2.2.3.3. A non-integer differentiator as a smoothing result; 2.2.3.4. A non-integer derivative as a water-dyke interface model; 2.3. From a non-integer derivative to a non-integer differential equation as a model governing water relaxation; 2.3.1. Flow-pressure differential equation
2.3.2. A non-integer differential equation as a model governing relaxation
Record Nr. UNINA-9910132160703321
Oustaloup Alain  
London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Diversity and non-integer differentiation for system dynamics / / Alain Oustaloup ; series editor Bernard Dubuisson
Diversity and non-integer differentiation for system dynamics / / Alain Oustaloup ; series editor Bernard Dubuisson
Autore Oustaloup Alain
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (383 p.)
Disciplina 003.85
Collana Control, Systems and Industrial Engineering Series
Soggetto topico Dynamics - Mathematical models
System analysis - Mathematical models
ISBN 1-118-76082-4
1-118-76086-7
1-118-76092-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page ; Copyright; Contents; Acknowledgments; Preface; Introduction; Chapter 1: From Diversity to Unexpected Dynamic Performances; 1.1. Introduction; 1.2. An issue raising a technological bottle-neck; 1.3. An aim liable to answer to the issue; 1.4. A strategy idea liable to reach the aim; 1.4.1. Why diversity?; 1.4.2. What does diversity imply?; 1.5. On the strategy itself; 1.5.1. The study object; 1.5.2. A pore: its model and its technological equivalent; 1.5.2.1. The model; 1.5.2.2. The technological equivalent; 1.5.3. Case of identical pores; 1.5.4. Case of different pores
1.5.4.1. On differences coming from regional heritage1.5.4.1.1 Differences of technological origin; 1.5.4.1.2. A difference of natural origin; 1.5.4.1.3. How is difference expressed?; 1.5.4.2. Transposition to the study object; 1.6. From physics to mathematics; 1.6.1. An unusual model of the porous face; 1.6.1.1. A smoothing remarkable of simplicity: the one of crenels; 1.6.1.2. A non-integer derivative as a smoothing result; 1.6.1.3. An original heuristic verification of differentiation non-integer order; 1.6.2. A just as unusual model governing water relaxation
1.7.2.1. Taking into account the past1.7.2.2. Memory notion; 1.7.2.3. A diversion through an aspect of human memory; 1.7.2.3.1. The serial position effect; 1.7.2.3.2. A model of the primacy effect; 1.8. On the nature of diversity; 1.8.1. An action level to be defined; 1.8.2. One or several forms of diversity?; 1.8.2.1. Forms based on the invariance of the elements; 1.8.2.2. A singular form based on the time variability of an element; 1.9. From the porous dyke to the CRONE suspension; 1.10. Conclusion; 1.11. Bibliography; Chapter 2: Damping Robustness; 2.1. Introduction
2.2. From ladder network to a non-integer derivative as a water-dyke interface model2.2.1. On the admittance factorizing; 2.2.2. On the asymptotic diagrams at stake; 2.2.3. On the asymptotic diagram exploiting; 2.2.3.1. Step smoothing; 2.2.3.2. Crenel smoothing; 2.2.3.3. A non-integer differentiator as a smoothing result; 2.2.3.4. A non-integer derivative as a water-dyke interface model; 2.3. From a non-integer derivative to a non-integer differential equation as a model governing water relaxation; 2.3.1. Flow-pressure differential equation
2.3.2. A non-integer differential equation as a model governing relaxation
Record Nr. UNINA-9910821362303321
Oustaloup Alain  
London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Grey systems analysis : methods, models and applications / / Sifeng Liu, Yingjie Yang, and Jeffrey Yi-Lin Forrest
Grey systems analysis : methods, models and applications / / Sifeng Liu, Yingjie Yang, and Jeffrey Yi-Lin Forrest
Autore Liu Sifeng
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (384 pages)
Disciplina 003
Collana Series on Grey System
Soggetto topico System analysis
System analysis - Mathematical models
Anàlisi de sistemes
Models matemàtics
Soggetto genere / forma Llibres electrònics
ISBN 981-19-6160-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Series Preface -- Foreword by Dr. James M. Tien -- Foreword by Dr. Keith William Hipel -- Foreword by Dr. Hermann Haken -- Foreword by Dr. Robert Vallée -- Preface -- Acknowledgements -- Contents -- 1 Introduction -- 1.1 The Scientific Background of the Birth of Grey System Theory -- 1.2 The Founder of Grey System Theory -- 1.3 Development of Grey Systems Theory -- 1.3.1 Building a Basic Team -- 1.3.2 Establishment of Academic Organizations -- 1.3.3 Journals and Book Series on Grey System Theory -- 1.3.4 Grey System Theory Curriculums -- 1.3.5 Researchers of Grey System Theory Are All Over the World -- 1.3.6 Papers of Grey Systems Theory Are Growing Rapidly -- 1.4 Elementary Concepts of Grey System Theory -- 1.5 Fundamental Principles of Grey System Theory -- 1.6 Main Contents of Grey System Theory -- References -- 2 Characteristics of Grey System Theory -- 2.1 A Kind of Poor Data Analysis Method with Strong Penetration -- 2.2 Characteristics of Uncertain Systems and the Simplicity Principle in Sciences -- 2.2.1 Incomplete Information -- 2.2.2 Inaccuracies in Data -- 2.2.3 The Scientific Principle of Simplicity -- 2.2.4 Precise Models Suffer from Inaccuracies -- 2.3 Comparison of Several Uncertainty Methods -- 2.4 Deep Applications of Grey System Theory in the Fields of Social Science, Natural Science and Engineering Technology -- 2.4.1 Successful Application of Grey System Theory in the Field of Social Sciences -- 2.4.2 Deep Application of Grey System Theory in the Field of Natural Science -- 2.4.3 A Large Number of Applications of Grey System Theory in the Field of Engineering Technology -- References -- 3 Grey Numbers and Their Operations -- 3.1 Grey Numbers -- 3.2 The Whitenization of a Grey Number and Degree of Greyness -- 3.3 Degree of Greyness Defined by Axioms -- 3.4 The Operations of Interval Grey Numbers.
3.5 General Grey Numbers and Their Operations -- 3.5.1 Reduced Form of Interval Grey Numbers -- 3.5.2 General Grey Number and Its Reduced Form -- 3.5.3 Synthesis of Degree of Greyness and Operations of General Grey Numbers -- References -- 4 Sequence Operators and Grey Data Mining -- 4.1 Introduction -- 4.2 Systems Under Shocking Disturbances and Buffer Operators -- 4.2.1 The Trap for Shocking Disturbed System Forecasting -- 4.2.2 Axioms of Buffer Operators -- 4.2.3 Properties of Buffer Operators -- 4.3 Construction of Practically Useful Buffer Operators -- 4.3.1 Weakening Buffer Operators -- 4.3.2 Strengthening Buffer Operators -- 4.3.3 The General Form of Buffer Operator -- 4.4 Average Operator -- 4.5 The Quasi-Smooth Sequence and Stepwise Ratio Operator -- 4.6 Accumulating and Inverse Accumulating Operators -- 4.7 Exponentiality of Accumulating Sequence -- References -- 5 Grey Relational Analysis Models -- 5.1 Introduction -- 5.2 Grey Relational Factors and Set of Grey Relational Operators -- 5.3 Grey Relational Axioms and Deng's Grey Relational Analysis Model -- 5.4 Grey Absolute Relational Degree -- 5.5 Grey Relative and Synthetic Relational Degree -- 5.5.1 Relative Grey Relational Degree -- 5.5.2 Grey Synthetic Relational Degree -- 5.6 Grey Similarity, Closeness and Three-Dimensional Relational Degree -- 5.6.1 Grey Relational Analysis Models Based on Similarity and Closeness -- 5.6.2 Grey Three-Dimension Degree of Relational Degree -- 5.7 Negative Grey Relational Analysis Models -- 5.8 Superiority Analysis -- 5.9 Practical Application -- References -- 6 Grey Clustering Evaluation Models -- 6.1 Introduction -- 6.2 Grey Relational Clustering Model -- 6.3 Common Possibility Functions -- 6.4 Variable Weight Grey Clustering Model -- 6.5 Fixed Weight Grey Clustering Model -- 6.6 Grey Clustering Evaluation Models Based on Mixed Possibility Functions.
6.6.1 Grey Clustering Evaluation Model Based on End-Point Mixed Possibility Functions -- 6.6.2 Grey Clustering Evaluation Model Based on Center-Point Mixed Possibility Functions -- 6.7 Practical Applications -- References -- 7 Series of GM Models -- 7.1 Introduction -- 7.2 The Four Basic Forms of GM(1,1) -- 7.2.1 The Basic Forms of Model GM(1,1) -- 7.2.2 Properties and Characteristics of the Basic Model -- 7.3 Suitable Ranges of Different GM(1,1) -- 7.3.1 Suitable Sequences of Different GM(1,1) -- 7.3.2 Applicable Ranges of EGM -- 7.4 Remnant GM(1,1) Model -- 7.5 Group of GM(1,1) Models -- 7.6 The Fractional Grey Model -- 7.7 The Models of GM(r,h) -- 7.7.1 The Model of GM(0,N) -- 7.7.2 The Model of GM(1, N) -- 7.7.3 The Grey Verhulst Model -- 7.7.4 The Self-memory Grey Model -- 7.7.5 The Models of GM(r,h) -- 7.8 Practical Applications -- References -- 8 Combined Grey Models -- 8.1 Grey Econometrics Models -- 8.1.1 Determination of Variables Using the Grey Relational Principles -- 8.1.2 Grey Econometrics Models -- 8.2 Combined Grey Linear Regression Models -- 8.3 Grey Cobb-Douglas Model -- 8.4 Grey Artificial Neural Network Models -- 8.4.1 BP Artificial Neural Model and Computational Schemes -- 8.4.2 Steps in Grey BP Neural Network Modeling -- 8.5 Grey Markov Model -- 8.5.1 Grey Moving Probability Markov Model -- 8.5.2 Grey State Markov Model -- 8.6 Combined Grey-Rough Model -- 8.6.1 Rough Membership, Grey Membership and Grey Numbers -- 8.6.2 Grey Rough Approximation -- 8.6.3 Combined Grey Clustering and Rough Set Model -- 8.7 Practical Applications -- References -- 9 Techniques for Grey Systems Forecasting -- 9.1 Introduction -- 9.2 Interval Forecasting -- 9.3 Grey Distortion Forecasting -- 9.4 Wave Form Forecasting -- 9.5 System Forecasting -- 9.5.1 The Five-Step Modeling Process -- 9.5.2 System Models for Prediction -- 9.6 Practical Applications.
References -- 10 Grey Models for Decision-Making -- 10.1 Introduction -- 10.2 Grey Target Decisions -- 10.3 Other Approaches to Grey Decision -- 10.3.1 Grey Relational Decision -- 10.3.2 Grey Development Decision -- 10.3.3 Grey Clustering Decision -- 10.4 Multi-attribute Intelligent Grey Target Decision Model -- 10.4.1 The Uniform Effect Measure -- 10.4.2 The Weighted Synthetic Effect Measure -- 10.5 On Paradox of Rule of Maximum Value and Its Solution -- 10.5.1 The Weight Vector Group with Kernel -- 10.5.2 The Weighted Comprehensive Clustering Coefficient Vector -- 10.5.3 Several Functional Weight Vector Groups with Kernel -- 10.6 Practical Applications -- References -- 11 Grey Control Systems -- 11.1 Introduction -- 11.2 Controllability and Observability of Grey System -- 11.3 Transfer Functions of Grey System -- 11.3.1 Grey Transfer Function -- 11.3.2 Transfer Functions of Typical Links -- 11.3.3 Matrices of Grey Transfer Functions -- 11.4 Robust Stability of Grey System -- 11.4.1 Robust Stability of Grey Linear Systems -- 11.4.2 Robust Stability of Grey Linear Time-Delay Systems -- 11.4.3 Robust Stability of Grey Stochastic Linear Time-Delay System -- 11.5 Several Typical Grey Control Models -- 11.5.1 Control of Redundancy Removal -- 11.5.2 Grey Relational Control -- 11.5.3 Control of Grey Prediction -- References -- 12 Spectrum Analysis of Sequence Operators -- 12.1 Introduction -- 12.2 Spectrum Analysis of Time Series Data -- 12.3 Filtering Effect of Mean Operator and Accumulation Operator -- 12.3.1 Filtering Effect of Mean Operator -- 12.3.2 Filtering Effect of Accumulation Operator -- 12.3.3 Filtering Effect of Series Operator -- 12.4 Spectrum Analysis of Buffer Operator -- References -- Appendix Introduction to Grey Systems Modeling Software -- A.1 Introduction -- A.2 Software Features and Functions -- A.3 Main Components.
A.4 Operation Guide -- A.4.1 The Confirmation System -- A.4.2 Using the Software Package -- Memorabilia of the Establishment and Development of Grey System Theory (1982-2021) -- Farewell to Our Tutor -- Bibliography -- Index.
Record Nr. UNISA-996503551703316
Liu Sifeng  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Grey systems analysis : methods, models and applications / / Sifeng Liu, Yingjie Yang, and Jeffrey Yi-Lin Forrest
Grey systems analysis : methods, models and applications / / Sifeng Liu, Yingjie Yang, and Jeffrey Yi-Lin Forrest
Autore Liu Sifeng
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (384 pages)
Disciplina 003
Collana Series on Grey System
Soggetto topico System analysis
System analysis - Mathematical models
Anàlisi de sistemes
Models matemàtics
Soggetto genere / forma Llibres electrònics
ISBN 981-19-6160-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Series Preface -- Foreword by Dr. James M. Tien -- Foreword by Dr. Keith William Hipel -- Foreword by Dr. Hermann Haken -- Foreword by Dr. Robert Vallée -- Preface -- Acknowledgements -- Contents -- 1 Introduction -- 1.1 The Scientific Background of the Birth of Grey System Theory -- 1.2 The Founder of Grey System Theory -- 1.3 Development of Grey Systems Theory -- 1.3.1 Building a Basic Team -- 1.3.2 Establishment of Academic Organizations -- 1.3.3 Journals and Book Series on Grey System Theory -- 1.3.4 Grey System Theory Curriculums -- 1.3.5 Researchers of Grey System Theory Are All Over the World -- 1.3.6 Papers of Grey Systems Theory Are Growing Rapidly -- 1.4 Elementary Concepts of Grey System Theory -- 1.5 Fundamental Principles of Grey System Theory -- 1.6 Main Contents of Grey System Theory -- References -- 2 Characteristics of Grey System Theory -- 2.1 A Kind of Poor Data Analysis Method with Strong Penetration -- 2.2 Characteristics of Uncertain Systems and the Simplicity Principle in Sciences -- 2.2.1 Incomplete Information -- 2.2.2 Inaccuracies in Data -- 2.2.3 The Scientific Principle of Simplicity -- 2.2.4 Precise Models Suffer from Inaccuracies -- 2.3 Comparison of Several Uncertainty Methods -- 2.4 Deep Applications of Grey System Theory in the Fields of Social Science, Natural Science and Engineering Technology -- 2.4.1 Successful Application of Grey System Theory in the Field of Social Sciences -- 2.4.2 Deep Application of Grey System Theory in the Field of Natural Science -- 2.4.3 A Large Number of Applications of Grey System Theory in the Field of Engineering Technology -- References -- 3 Grey Numbers and Their Operations -- 3.1 Grey Numbers -- 3.2 The Whitenization of a Grey Number and Degree of Greyness -- 3.3 Degree of Greyness Defined by Axioms -- 3.4 The Operations of Interval Grey Numbers.
3.5 General Grey Numbers and Their Operations -- 3.5.1 Reduced Form of Interval Grey Numbers -- 3.5.2 General Grey Number and Its Reduced Form -- 3.5.3 Synthesis of Degree of Greyness and Operations of General Grey Numbers -- References -- 4 Sequence Operators and Grey Data Mining -- 4.1 Introduction -- 4.2 Systems Under Shocking Disturbances and Buffer Operators -- 4.2.1 The Trap for Shocking Disturbed System Forecasting -- 4.2.2 Axioms of Buffer Operators -- 4.2.3 Properties of Buffer Operators -- 4.3 Construction of Practically Useful Buffer Operators -- 4.3.1 Weakening Buffer Operators -- 4.3.2 Strengthening Buffer Operators -- 4.3.3 The General Form of Buffer Operator -- 4.4 Average Operator -- 4.5 The Quasi-Smooth Sequence and Stepwise Ratio Operator -- 4.6 Accumulating and Inverse Accumulating Operators -- 4.7 Exponentiality of Accumulating Sequence -- References -- 5 Grey Relational Analysis Models -- 5.1 Introduction -- 5.2 Grey Relational Factors and Set of Grey Relational Operators -- 5.3 Grey Relational Axioms and Deng's Grey Relational Analysis Model -- 5.4 Grey Absolute Relational Degree -- 5.5 Grey Relative and Synthetic Relational Degree -- 5.5.1 Relative Grey Relational Degree -- 5.5.2 Grey Synthetic Relational Degree -- 5.6 Grey Similarity, Closeness and Three-Dimensional Relational Degree -- 5.6.1 Grey Relational Analysis Models Based on Similarity and Closeness -- 5.6.2 Grey Three-Dimension Degree of Relational Degree -- 5.7 Negative Grey Relational Analysis Models -- 5.8 Superiority Analysis -- 5.9 Practical Application -- References -- 6 Grey Clustering Evaluation Models -- 6.1 Introduction -- 6.2 Grey Relational Clustering Model -- 6.3 Common Possibility Functions -- 6.4 Variable Weight Grey Clustering Model -- 6.5 Fixed Weight Grey Clustering Model -- 6.6 Grey Clustering Evaluation Models Based on Mixed Possibility Functions.
6.6.1 Grey Clustering Evaluation Model Based on End-Point Mixed Possibility Functions -- 6.6.2 Grey Clustering Evaluation Model Based on Center-Point Mixed Possibility Functions -- 6.7 Practical Applications -- References -- 7 Series of GM Models -- 7.1 Introduction -- 7.2 The Four Basic Forms of GM(1,1) -- 7.2.1 The Basic Forms of Model GM(1,1) -- 7.2.2 Properties and Characteristics of the Basic Model -- 7.3 Suitable Ranges of Different GM(1,1) -- 7.3.1 Suitable Sequences of Different GM(1,1) -- 7.3.2 Applicable Ranges of EGM -- 7.4 Remnant GM(1,1) Model -- 7.5 Group of GM(1,1) Models -- 7.6 The Fractional Grey Model -- 7.7 The Models of GM(r,h) -- 7.7.1 The Model of GM(0,N) -- 7.7.2 The Model of GM(1, N) -- 7.7.3 The Grey Verhulst Model -- 7.7.4 The Self-memory Grey Model -- 7.7.5 The Models of GM(r,h) -- 7.8 Practical Applications -- References -- 8 Combined Grey Models -- 8.1 Grey Econometrics Models -- 8.1.1 Determination of Variables Using the Grey Relational Principles -- 8.1.2 Grey Econometrics Models -- 8.2 Combined Grey Linear Regression Models -- 8.3 Grey Cobb-Douglas Model -- 8.4 Grey Artificial Neural Network Models -- 8.4.1 BP Artificial Neural Model and Computational Schemes -- 8.4.2 Steps in Grey BP Neural Network Modeling -- 8.5 Grey Markov Model -- 8.5.1 Grey Moving Probability Markov Model -- 8.5.2 Grey State Markov Model -- 8.6 Combined Grey-Rough Model -- 8.6.1 Rough Membership, Grey Membership and Grey Numbers -- 8.6.2 Grey Rough Approximation -- 8.6.3 Combined Grey Clustering and Rough Set Model -- 8.7 Practical Applications -- References -- 9 Techniques for Grey Systems Forecasting -- 9.1 Introduction -- 9.2 Interval Forecasting -- 9.3 Grey Distortion Forecasting -- 9.4 Wave Form Forecasting -- 9.5 System Forecasting -- 9.5.1 The Five-Step Modeling Process -- 9.5.2 System Models for Prediction -- 9.6 Practical Applications.
References -- 10 Grey Models for Decision-Making -- 10.1 Introduction -- 10.2 Grey Target Decisions -- 10.3 Other Approaches to Grey Decision -- 10.3.1 Grey Relational Decision -- 10.3.2 Grey Development Decision -- 10.3.3 Grey Clustering Decision -- 10.4 Multi-attribute Intelligent Grey Target Decision Model -- 10.4.1 The Uniform Effect Measure -- 10.4.2 The Weighted Synthetic Effect Measure -- 10.5 On Paradox of Rule of Maximum Value and Its Solution -- 10.5.1 The Weight Vector Group with Kernel -- 10.5.2 The Weighted Comprehensive Clustering Coefficient Vector -- 10.5.3 Several Functional Weight Vector Groups with Kernel -- 10.6 Practical Applications -- References -- 11 Grey Control Systems -- 11.1 Introduction -- 11.2 Controllability and Observability of Grey System -- 11.3 Transfer Functions of Grey System -- 11.3.1 Grey Transfer Function -- 11.3.2 Transfer Functions of Typical Links -- 11.3.3 Matrices of Grey Transfer Functions -- 11.4 Robust Stability of Grey System -- 11.4.1 Robust Stability of Grey Linear Systems -- 11.4.2 Robust Stability of Grey Linear Time-Delay Systems -- 11.4.3 Robust Stability of Grey Stochastic Linear Time-Delay System -- 11.5 Several Typical Grey Control Models -- 11.5.1 Control of Redundancy Removal -- 11.5.2 Grey Relational Control -- 11.5.3 Control of Grey Prediction -- References -- 12 Spectrum Analysis of Sequence Operators -- 12.1 Introduction -- 12.2 Spectrum Analysis of Time Series Data -- 12.3 Filtering Effect of Mean Operator and Accumulation Operator -- 12.3.1 Filtering Effect of Mean Operator -- 12.3.2 Filtering Effect of Accumulation Operator -- 12.3.3 Filtering Effect of Series Operator -- 12.4 Spectrum Analysis of Buffer Operator -- References -- Appendix Introduction to Grey Systems Modeling Software -- A.1 Introduction -- A.2 Software Features and Functions -- A.3 Main Components.
A.4 Operation Guide -- A.4.1 The Confirmation System -- A.4.2 Using the Software Package -- Memorabilia of the Establishment and Development of Grey System Theory (1982-2021) -- Farewell to Our Tutor -- Bibliography -- Index.
Record Nr. UNINA-9910634032803321
Liu Sifeng  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of real-world applications in modeling and simulation [[electronic resource] /] / edited by John A. Sokolowski, Catherine M. Banks
Handbook of real-world applications in modeling and simulation [[electronic resource] /] / edited by John A. Sokolowski, Catherine M. Banks
Autore Sokolowski John A. <1953->
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2012
Descrizione fisica 1 online resource (351 p.)
Disciplina 003
Altri autori (Persone) SokolowskiJohn A. <1953->
BanksCatherine M. <1960->
Collana Wiley handbooks in operations research and management science
Soggetto topico System analysis - Mathematical models
Computer simulation
ISBN 1-280-68535-2
9786613662293
1-118-24126-6
1-118-24104-5
1-118-24129-0
Classificazione MAT000000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Contributors: Preface -- Introduction -- Contemplating a National Strategy for Modeling and Simulation / John A. Sokolowski -- Chapter 1. Modeling and Simulation for Research and Analysis / Catherine M. Banks -- Chapter 2. Human Behavior Modeling: A Real-World Application / John A. Sokolowski -- Chapter 3. Transportation / R. Michael Robinson -- Chapter 4. Homeland Security Risk Modeling / Barry C. Ezell -- Chapter 5. Operations Research /Andrew J. Collins and Christine S. M. Currie -- Chapter 6. Business Process Modeling / Rafael Diaz, Joshua G. Behr, and Mandur Tupule -- Chapter 7. Medical M&S: A Review of Mesh Generation for Medical Simulators / Michele A. Audette, Andrey N. Chernikov, and Nikos Chrisochoides -- Chapter 8. Military Interoperability Challenges / Siakou Diallo and Jose Padilla -- Appendices -- PowerPoint Slides -- Index.
Record Nr. UNINA-9910141294803321
Sokolowski John A. <1953->  
Hoboken, N.J., : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of real-world applications in modeling and simulation / / edited by John A. Sokolowski, Catherine M. Banks
Handbook of real-world applications in modeling and simulation / / edited by John A. Sokolowski, Catherine M. Banks
Autore Sokolowski John A. <1953->
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2012
Descrizione fisica 1 online resource (351 p.)
Disciplina 003
Altri autori (Persone) SokolowskiJohn A. <1953->
BanksCatherine M. <1960->
Collana Wiley handbooks in operations research and management science
Soggetto topico System analysis - Mathematical models
Computer simulation
ISBN 1-280-68535-2
9786613662293
1-118-24126-6
1-118-24104-5
1-118-24129-0
Classificazione MAT000000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Contributors: Preface -- Introduction -- Contemplating a National Strategy for Modeling and Simulation / John A. Sokolowski -- Chapter 1. Modeling and Simulation for Research and Analysis / Catherine M. Banks -- Chapter 2. Human Behavior Modeling: A Real-World Application / John A. Sokolowski -- Chapter 3. Transportation / R. Michael Robinson -- Chapter 4. Homeland Security Risk Modeling / Barry C. Ezell -- Chapter 5. Operations Research /Andrew J. Collins and Christine S. M. Currie -- Chapter 6. Business Process Modeling / Rafael Diaz, Joshua G. Behr, and Mandur Tupule -- Chapter 7. Medical M&S: A Review of Mesh Generation for Medical Simulators / Michele A. Audette, Andrey N. Chernikov, and Nikos Chrisochoides -- Chapter 8. Military Interoperability Challenges / Siakou Diallo and Jose Padilla -- Appendices -- PowerPoint Slides -- Index.
Record Nr. UNINA-9910827006803321
Sokolowski John A. <1953->  
Hoboken, N.J., : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma
Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma
Pubbl/distr/stampa Piscataway, NJ : , : IEEE Press
Descrizione fisica 1 online resource (224 p.)
Disciplina 006.312
Soggetto topico Data mining
Information resources management
Information resources - Evaluation
System analysis - Mathematical models
ISBN 1-118-64633-9
1-118-64620-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface ix -- Contributors xi -- 1 Introduction 1 -- Haibo He -- 1.1 Problem Formulation, 1 -- 1.2 State-of-the-Art Research, 3 -- 1.3 Looking Ahead: Challenges and Opportunities, 6 -- 1.4 Acknowledgments, 7 -- References, 8 -- 2 Foundations of Imbalanced Learning 13 -- Gary M. Weiss -- 2.1 Introduction, 14 -- 2.2 Background, 14 -- 2.3 Foundational Issues, 19 -- 2.4 Methods for Addressing Imbalanced Data, 26 -- 2.5 Mapping Foundational Issues to Solutions, 35 -- 2.6 Misconceptions About Sampling Methods, 36 -- 2.7 Recommendations and Guidelines, 38 -- References, 38 -- 3 Imbalanced Datasets: From Sampling to Classifiers 43 -- T. Ryan Hoens and Nitesh V. Chawla -- 3.1 Introduction, 43 -- 3.2 Sampling Methods, 44 -- 3.3 Skew-Insensitive Classifiers for Class Imbalance, 49 -- 3.4 Evaluation Metrics, 52 -- 3.5 Discussion, 56 -- References, 57 -- 4 Ensemble Methods for Class Imbalance Learning 61 -- Xu-Ying Liu and Zhi-Hua Zhou -- 4.1 Introduction, 61 -- 4.2 Ensemble Methods, 62 -- 4.3 Ensemble Methods for Class Imbalance Learning, 66 -- 4.4 Empirical Study, 73 -- 4.5 Concluding Remarks, 79 -- References, 80 -- 5 Class Imbalance Learning Methods for Support Vector Machines 83 -- Rukshan Batuwita and Vasile Palade -- 5.1 Introduction, 83 -- 5.2 Introduction to Support Vector Machines, 84 -- 5.3 SVMs and Class Imbalance, 86 -- 5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods, 87 -- 5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 88 -- 5.6 Summary, 96 -- References, 96 -- 6 Class Imbalance and Active Learning 101 -- Josh Attenberg and Sd eyda Ertekin -- 6.1 Introduction, 102 -- 6.2 Active Learning for Imbalanced Problems, 103 -- 6.3 Active Learning for Imbalanced Data Classification, 110 -- 6.4 Adaptive Resampling with Active Learning, 122 -- 6.5 Difficulties with Extreme Class Imbalance, 129 -- 6.6 Dealing with Disjunctive Classes, 130 -- 6.7 Starting Cold, 132 -- 6.8 Alternatives to Active Learning for Imbalanced Problems, 133.
6.9 Conclusion, 144 -- References, 145 -- 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151 -- Sheng Chen and Haibo He -- 7.1 Introduction, 152 -- 7.2 Preliminaries, 154 -- 7.3 Algorithms, 157 -- 7.4 Simulation, 167 -- 7.5 Conclusion, 182 -- 7.6 Acknowledgments, 183 -- References, 184 -- 8 Assessment Metrics for Imbalanced Learning 187 -- Nathalie Japkowicz -- 8.1 Introduction, 187 -- 8.2 A Review of Evaluation Metric Families and their Applicability -- to the Class Imbalance Problem, 189 -- 8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190 -- 8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196 -- 8.5 Conclusion, 204 -- 8.6 Acknowledgments, 205 -- References, 205 -- Index 207.
Record Nr. UNINA-9910141572403321
Piscataway, NJ : , : IEEE Press
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma
Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma
Pubbl/distr/stampa Piscataway, NJ : , : IEEE Press
Descrizione fisica 1 online resource (224 p.)
Disciplina 006.312
Soggetto topico Data mining
Information resources management
Information resources - Evaluation
System analysis - Mathematical models
ISBN 1-118-64633-9
1-118-64620-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface ix -- Contributors xi -- 1 Introduction 1 -- Haibo He -- 1.1 Problem Formulation, 1 -- 1.2 State-of-the-Art Research, 3 -- 1.3 Looking Ahead: Challenges and Opportunities, 6 -- 1.4 Acknowledgments, 7 -- References, 8 -- 2 Foundations of Imbalanced Learning 13 -- Gary M. Weiss -- 2.1 Introduction, 14 -- 2.2 Background, 14 -- 2.3 Foundational Issues, 19 -- 2.4 Methods for Addressing Imbalanced Data, 26 -- 2.5 Mapping Foundational Issues to Solutions, 35 -- 2.6 Misconceptions About Sampling Methods, 36 -- 2.7 Recommendations and Guidelines, 38 -- References, 38 -- 3 Imbalanced Datasets: From Sampling to Classifiers 43 -- T. Ryan Hoens and Nitesh V. Chawla -- 3.1 Introduction, 43 -- 3.2 Sampling Methods, 44 -- 3.3 Skew-Insensitive Classifiers for Class Imbalance, 49 -- 3.4 Evaluation Metrics, 52 -- 3.5 Discussion, 56 -- References, 57 -- 4 Ensemble Methods for Class Imbalance Learning 61 -- Xu-Ying Liu and Zhi-Hua Zhou -- 4.1 Introduction, 61 -- 4.2 Ensemble Methods, 62 -- 4.3 Ensemble Methods for Class Imbalance Learning, 66 -- 4.4 Empirical Study, 73 -- 4.5 Concluding Remarks, 79 -- References, 80 -- 5 Class Imbalance Learning Methods for Support Vector Machines 83 -- Rukshan Batuwita and Vasile Palade -- 5.1 Introduction, 83 -- 5.2 Introduction to Support Vector Machines, 84 -- 5.3 SVMs and Class Imbalance, 86 -- 5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods, 87 -- 5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 88 -- 5.6 Summary, 96 -- References, 96 -- 6 Class Imbalance and Active Learning 101 -- Josh Attenberg and Sd eyda Ertekin -- 6.1 Introduction, 102 -- 6.2 Active Learning for Imbalanced Problems, 103 -- 6.3 Active Learning for Imbalanced Data Classification, 110 -- 6.4 Adaptive Resampling with Active Learning, 122 -- 6.5 Difficulties with Extreme Class Imbalance, 129 -- 6.6 Dealing with Disjunctive Classes, 130 -- 6.7 Starting Cold, 132 -- 6.8 Alternatives to Active Learning for Imbalanced Problems, 133.
6.9 Conclusion, 144 -- References, 145 -- 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151 -- Sheng Chen and Haibo He -- 7.1 Introduction, 152 -- 7.2 Preliminaries, 154 -- 7.3 Algorithms, 157 -- 7.4 Simulation, 167 -- 7.5 Conclusion, 182 -- 7.6 Acknowledgments, 183 -- References, 184 -- 8 Assessment Metrics for Imbalanced Learning 187 -- Nathalie Japkowicz -- 8.1 Introduction, 187 -- 8.2 A Review of Evaluation Metric Families and their Applicability -- to the Class Imbalance Problem, 189 -- 8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190 -- 8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196 -- 8.5 Conclusion, 204 -- 8.6 Acknowledgments, 205 -- References, 205 -- Index 207.
Record Nr. UNINA-9910830339103321
Piscataway, NJ : , : IEEE Press
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Information path functional and informational macrodynamics [[electronic resource] /] / Vladimir S. Lerner
Information path functional and informational macrodynamics [[electronic resource] /] / Vladimir S. Lerner
Autore Lerner Vladimir S. <1931->
Pubbl/distr/stampa Hauppauge, N.Y., : Nova Science Publishers, c2010
Descrizione fisica 1 online resource (507 p.)
Disciplina 629.8/312
Soggetto topico Stochastic control theory
Variational principles
System analysis - Mathematical models
Soggetto genere / forma Electronic books.
ISBN 1-61470-092-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. 1. The information path functional's foundation -- pt. 2. The information path functional's and IMD's applications.
Record Nr. UNINA-9910457696903321
Lerner Vladimir S. <1931->  
Hauppauge, N.Y., : Nova Science Publishers, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Information path functional and informational macrodynamics [[electronic resource] /] / Vladimir S. Lerner
Information path functional and informational macrodynamics [[electronic resource] /] / Vladimir S. Lerner
Autore Lerner Vladimir S. <1931->
Pubbl/distr/stampa Hauppauge, N.Y., : Nova Science Publishers, c2010
Descrizione fisica 1 online resource (507 p.)
Disciplina 629.8/312
Soggetto topico Stochastic control theory
Variational principles
System analysis - Mathematical models
ISBN 1-61470-092-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. 1. The information path functional's foundation -- pt. 2. The information path functional's and IMD's applications.
Record Nr. UNINA-9910781856303321
Lerner Vladimir S. <1931->  
Hauppauge, N.Y., : Nova Science Publishers, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui