Quantum inspired intelligent systems / Nadia Nedjah, Leandro dos Santos Coelho, Luiza de Macedo Mourelle (eds.).
Material type:
TextPublication details: Berlin : Springer, 2008Description: xiv, 153 p. 25 cmISBN: 9783540785316 (hardcover : alk. paper)Subject(s): Quantum computersDDC classification: 004.1 | Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
General Books
|
Central Library, Sikkim University General Book Section | 004.1 (Browse shelf(Opens below)) | Available | P42495 |
1 Gaussian Quantum-Behaved Particle Swarm Optimization
Applied to Fuzzy FID Controller Design
Leandro dos Santos Coelho, Nadia Nedjah, Luiza de Macedo Mourelle
1.1 Introduction
1.2 Fuzzy Control Algorithm .
1.2.1 Fuzzy PID controller
1.3 Quantum-behaved Particle Swarm Optimization
1.3.1 QPSO using Gaussian distribution
1.4 Case Study and Simulation Results
1.4.1 Description of Control Valve
1.4.2 Simulation results
1.5 Conclusion and Future Work
2 Quantum-inspired genetic algorithms for flow shop
scheduling
Ling Wang^ Bin-bin Li.
2.1 Introduction
2.2 Permutation flow shop scheduling
2.2.1 Problem statement
2.2.2 GA-based multi-objective PFSS .
2.3 Quantum-inspired genetic algorithm
2.4 Hybrid QGA for single-objective PFSS
2.4.1 QGA for single-objective PFSS
2.4.2 Permutation-based GA
2.4.3 Hybrid QGA for PFSS
2.5 Hybrid QGA for multi-objective FSSP
2.5.1 Solution Evaluation in Multi-objective Sense
2.5.2 Population Trimming in QGA and PGA
2.5.3 Genetic Operators and Stopping Criteria in QGA and PGA .
2.5.4 Population Upadting in QGA and PGA
VI Contents
2.5.5 Procedure of QGA and PGA
2.5.6 Hybrid Quantum-Inspired Genetic Algorithm
2.6 Performance metrics of multi-objective optimization
2.6.1 Overall Non-dominated Vector Generation {ONVG)
2.6.2 C Metric (CM)
2.6.3 Distance iMetrics {Dav and Dmax)
2.6.4 Tan's Spacing (TS)
2.6.5 Maximum Spread {MS)
2.6.6 Average Quality {AQ)
2.7 Simulation results and comparisons on single-objective PFFS
2.8 Simulation results and comparisons on multi-objective PFFS
2.8.1 Testing on existing problems in the literature
2.8.2 Testing and comparisons on randomly generated instances
2.9 Conclusions and future work
]
3 Quantum Simulataneous Recurrent Networks for Content
Addressable Memory
Raheel Allauddin, Stuart Boehmer, Elizabeth C. Behrman, Kavitha
Gaddam, James E. Steak
3.1 Introduction
3.2 The Hopfield Model
3.3 THE QUANTUM SYSTEM
3.4 Mapping the Quantum System onto the Hopheld iNet
3.5 QHN As An Information Propagator for a Microtubulin Architecture
3.6 QHN As Simultaneous Recurrent Network
3.7 Application: The CNOT Gate
4 Quantum Intelligent Mobile System
Chunlin Chen, Daoyi Dong
4.1 Introduction
4.2 Prerequisite
4.2.1 Intelligent mobile system
4.2.2 Fundamentals of quantum mechanics
4.3 Architecture of QIMS based on multi-quantum-agent
4.3.1 Intelligent agent in quantum system
4.3.2 Multi-quantum-agent system
4.3.3 Architecture of QIMS based on MQAS
4.4 Hardware of typical QIMS
4.4.1 Quantum computers
4.4.2 Classical-quantum hybrid components
4.5 Planning for QIMS based on Grover algorithm
4.5.1 A general framework of planning tasks .
4.5.2 Grover algorithm
4.5.3 Planning using Grover algorithm
4.6 Learning for QIMS using QRL
4.6.1 QRL
4.6.2 Learning in unknown environment using QRL
4.7 Discussion or ^he applications of QIMS
4.8 Conclusions
5 Quantum Associative Pattern Retrieval
Carlo A. Trugenberger, Cristina M. Diamantini.
5.1 Introduction
5.2 Quantizing Neural Networks
5.3 Quantum Associative Memories
5.4 Phase Structure
5.5 Summary
6 Quantum-Inspired Evolutionary Algorithm for Numerical
Optimization
Andre Vargas Aba da Cruz, Marley M. B. R. Vellasco, Marco Aurelio
C. Pacheco
6.1 Introduction
6.2 The Quantum-Inspired Evolutionary Algorithm using a Real
Number Representation
6.2.1 The Quantum Population
6.2.2 Quantum Individuals Observation
6.2.3 Updating the Quantum Population
6.3 Case Studies
6.3.1 Optimization of Benchmark Functions
6.3.2 Discussion
6.4 Conclusions and Future Works
7 Calibration of the VGSSD Option Pricing Model
using a Quantum-inspired Evolutionary Algorithm
Kai Fan, Conall O'Sullivan, Anthony Brabazon, Michael O'Neill, Sean
McCarraghy
7.1 Introduction
7.2 The Quantum-inspired Genetic Algorithm
7.2.1 Representing a Quantum System
7.2.2 Real-valued quantum-inspired evolutionary algorithms.
7.3 Option Pricing Model Calibration
7.4 Experimental Approach
7.5 Results
7.6 Conclusions1 Gaussian Quantum-Behaved Particle Swarm Optimization
Applied to Fuzzy FID Controller Design
Leandro dos Santos Coelho, Nadia Nedjah, Luiza de Macedo Mourelle
1.1 Introduction
1.2 Fuzzy Control Algorithm .
1.2.1 Fuzzy PID controller
1.3 Quantum-behaved Particle Swarm Optimization
1.3.1 QPSO using Gaussian distribution
1.4 Case Study and Simulation Results
1.4.1 Description of Control Valve
1.4.2 Simulation results
1.5 Conclusion and Future Work
2 Quantum-inspired genetic algorithms for flow shop
scheduling
Ling Wang^ Bin-bin Li.
2.1 Introduction
2.2 Permutation flow shop scheduling
2.2.1 Problem statement
2.2.2 GA-based multi-objective PFSS .
2.3 Quantum-inspired genetic algorithm
2.4 Hybrid QGA for single-objective PFSS
2.4.1 QGA for single-objective PFSS
2.4.2 Permutation-based GA
2.4.3 Hybrid QGA for PFSS
2.5 Hybrid QGA for multi-objective FSSP
2.5.1 Solution Evaluation in Multi-objective Sense
2.5.2 Population Trimming in QGA and PGA
2.5.3 Genetic Operators and Stopping Criteria in QGA and PGA .
2.5.4 Population Upadting in QGA and PGA
VI Contents
2.5.5 Procedure of QGA and PGA
2.5.6 Hybrid Quantum-Inspired Genetic Algorithm
2.6 Performance metrics of multi-objective optimization
2.6.1 Overall Non-dominated Vector Generation {ONVG)
2.6.2 C Metric (CM)
2.6.3 Distance iMetrics {Dav and Dmax)
2.6.4 Tan's Spacing (TS)
2.6.5 Maximum Spread {MS)
2.6.6 Average Quality {AQ)
2.7 Simulation results and comparisons on single-objective PFFS
2.8 Simulation results and comparisons on multi-objective PFFS
2.8.1 Testing on existing problems in the literature
2.8.2 Testing and comparisons on randomly generated instances
2.9 Conclusions and future work
]
3 Quantum Simulataneous Recurrent Networks for Content
Addressable Memory
Raheel Allauddin, Stuart Boehmer, Elizabeth C. Behrman, Kavitha
Gaddam, James E. Steak
3.1 Introduction
3.2 The Hopfield Model
3.3 THE QUANTUM SYSTEM
3.4 Mapping the Quantum System onto the Hopheld iNet
3.5 QHN As An Information Propagator for a Microtubulin Architecture
3.6 QHN As Simultaneous Recurrent Network
3.7 Application: The CNOT Gate
4 Quantum Intelligent Mobile System
Chunlin Chen, Daoyi Dong
4.1 Introduction
4.2 Prerequisite
4.2.1 Intelligent mobile system
4.2.2 Fundamentals of quantum mechanics
4.3 Architecture of QIMS based on multi-quantum-agent
4.3.1 Intelligent agent in quantum system
4.3.2 Multi-quantum-agent system
4.3.3 Architecture of QIMS based on MQAS
4.4 Hardware of typical QIMS
4.4.1 Quantum computers
4.4.2 Classical-quantum hybrid components
4.5 Planning for QIMS based on Grover algorithm
4.5.1 A general framework of planning tasks .
4.5.2 Grover algorithm
4.5.3 Planning using Grover algorithm
4.6 Learning for QIMS using QRL
4.6.1 QRL
4.6.2 Learning in unknown environment using QRL
4.7 Discussion or ^he applications of QIMS
4.8 Conclusions
5 Quantum Associative Pattern Retrieval
Carlo A. Trugenberger, Cristina M. Diamantini.
5.1 Introduction
5.2 Quantizing Neural Networks
5.3 Quantum Associative Memories
5.4 Phase Structure
5.5 Summary
6 Quantum-Inspired Evolutionary Algorithm for Numerical
Optimization
Andre Vargas Aba da Cruz, Marley M. B. R. Vellasco, Marco Aurelio
C. Pacheco
6.1 Introduction
6.2 The Quantum-Inspired Evolutionary Algorithm using a Real
Number Representation
6.2.1 The Quantum Population
6.2.2 Quantum Individuals Observation
6.2.3 Updating the Quantum Population
6.3 Case Studies
6.3.1 Optimization of Benchmark Functions
6.3.2 Discussion
6.4 Conclusions and Future Works
7 Calibration of the VGSSD Option Pricing Model
using a Quantum-inspired Evolutionary Algorithm
Kai Fan, Conall O'Sullivan, Anthony Brabazon, Michael O'Neill, Sean
McCarraghy
7.1 Introduction
7.2 The Quantum-inspired Genetic Algorithm
7.2.1 Representing a Quantum System
7.2.2 Real-valued quantum-inspired evolutionary algorithms.
7.3 Option Pricing Model Calibration
7.4 Experimental Approach
7.5 Results
7.6 Conclusions1 Gaussian Quantum-Behaved Particle Swarm Optimization
Applied to Fuzzy FID Controller Design
Leandro dos Santos Coelho, Nadia Nedjah, Luiza de Macedo Mourelle
1.1 Introduction
1.2 Fuzzy Control Algorithm .
1.2.1 Fuzzy PID controller
1.3 Quantum-behaved Particle Swarm Optimization
1.3.1 QPSO using Gaussian distribution
1.4 Case Study and Simulation Results
1.4.1 Description of Control Valve
1.4.2 Simulation results
1.5 Conclusion and Future Work
2 Quantum-inspired genetic algorithms for flow shop
scheduling
Ling Wang^ Bin-bin Li.
2.1 Introduction
2.2 Permutation flow shop scheduling
2.2.1 Problem statement
2.2.2 GA-based multi-objective PFSS .
2.3 Quantum-inspired genetic algorithm
2.4 Hybrid QGA for single-objective PFSS
2.4.1 QGA for single-objective PFSS
2.4.2 Permutation-based GA
2.4.3 Hybrid QGA for PFSS
2.5 Hybrid QGA for multi-objective FSSP
2.5.1 Solution Evaluation in Multi-objective Sense
2.5.2 Population Trimming in QGA and PGA
2.5.3 Genetic Operators and Stopping Criteria in QGA and PGA .
2.5.4 Population Upadting in QGA and PGA
VI Contents
2.5.5 Procedure of QGA and PGA
2.5.6 Hybrid Quantum-Inspired Genetic Algorithm
2.6 Performance metrics of multi-objective optimization
2.6.1 Overall Non-dominated Vector Generation {ONVG)
2.6.2 C Metric (CM)
2.6.3 Distance iMetrics {Dav and Dmax)
2.6.4 Tan's Spacing (TS)
2.6.5 Maximum Spread {MS)
2.6.6 Average Quality {AQ)
2.7 Simulation results and comparisons on single-objective PFFS
2.8 Simulation results and comparisons on multi-objective PFFS
2.8.1 Testing on existing problems in the literature
2.8.2 Testing and comparisons on randomly generated instances
2.9 Conclusions and future work
]
3 Quantum Simulataneous Recurrent Networks for Content
Addressable Memory
Raheel Allauddin, Stuart Boehmer, Elizabeth C. Behrman, Kavitha
Gaddam, James E. Steak
3.1 Introduction
3.2 The Hopfield Model
3.3 THE QUANTUM SYSTEM
3.4 Mapping the Quantum System onto the Hopheld iNet
3.5 QHN As An Information Propagator for a Microtubulin Architecture
3.6 QHN As Simultaneous Recurrent Network
3.7 Application: The CNOT Gate
4 Quantum Intelligent Mobile System
Chunlin Chen, Daoyi Dong
4.1 Introduction
4.2 Prerequisite
4.2.1 Intelligent mobile system
4.2.2 Fundamentals of quantum mechanics
4.3 Architecture of QIMS based on multi-quantum-agent
4.3.1 Intelligent agent in quantum system
4.3.2 Multi-quantum-agent system
4.3.3 Architecture of QIMS based on MQAS
4.4 Hardware of typical QIMS
4.4.1 Quantum computers
4.4.2 Classical-quantum hybrid components
4.5 Planning for QIMS based on Grover algorithm
4.5.1 A general framework of planning tasks .
4.5.2 Grover algorithm
4.5.3 Planning using Grover algorithm
4.6 Learning for QIMS using QRL
4.6.1 QRL
4.6.2 Learning in unknown environment using QRL
4.7 Discussion or ^he applications of QIMS
4.8 Conclusions
5 Quantum Associative Pattern Retrieval
Carlo A. Trugenberger, Cristina M. Diamantini.
5.1 Introduction
5.2 Quantizing Neural Networks
5.3 Quantum Associative Memories
5.4 Phase Structure
5.5 Summary
6 Quantum-Inspired Evolutionary Algorithm for Numerical
Optimization
Andre Vargas Aba da Cruz, Marley M. B. R. Vellasco, Marco Aurelio
C. Pacheco
6.1 Introduction
6.2 The Quantum-Inspired Evolutionary Algorithm using a Real
Number Representation
6.2.1 The Quantum Population
6.2.2 Quantum Individuals Observation
6.2.3 Updating the Quantum Population
6.3 Case Studies
6.3.1 Optimization of Benchmark Functions
6.3.2 Discussion
6.4 Conclusions and Future Works
7 Calibration of the VGSSD Option Pricing Model
using a Quantum-inspired Evolutionary Algorithm
Kai Fan, Conall O'Sullivan, Anthony Brabazon, Michael O'Neill, Sean
McCarraghy
7.1 Introduction
7.2 The Quantum-inspired Genetic Algorithm
7.2.1 Representing a Quantum System
7.2.2 Real-valued quantum-inspired evolutionary algorithms.
7.3 Option Pricing Model Calibration
7.4 Experimental Approach
7.5 Results
7.6 Conclusions
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