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Computational Intelligence in Complex Decision Systems / by Da Ruan
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Atlantis Computational Intelligence Systems ; 2 | SpringerLink BücherPublisher: Paris : Atlantis Press, 2010Description: Online-Ressource (XIV, 388p, digital)ISBN:- 9789491216299
- 006.3
- 500
- Q334-342 TJ210.2-211.495
- Q334-342
- TJ210.2-211.495
Contents:
Summary: In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systemsSummary: In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systems.PPN: PPN: 1651282595Package identifier: Produktsigel: ZDB-2-SCS
Computational Intelligence in Complex Decision Systems; Preface; Contents; Chapter 1: Computational Intelligence: Past, Today, and Future; 1.1 Introduction; 1.2 Neural Networks; 1.2.1 Network Architectures; 1.2.2 Learning processes; 1.3 Fuzzy Set Theory; 1.4 Evolutionary Computation; 1.4.1 Genetic algorithms; 1.4.2 Genetic programming; 1.4.3 Evolution strategies; 1.4.4 Evolutionary programming; 1.4.5 Classifier systems; 1.4.6 Ant colony optimization; 1.4.7 Particle swarm optimization; 1.5 Hybrid Systems; 1.6 Literature Review; 1.6.1 Computational intelligence in complex decision systems
1.7 Computational Intelligence Journals1.8 Computational Intelligence's Future and Conclusions; Bibliography; Chapter 2: Uncertainty in Dynamically Changing Input Data; 2.1 Problem Statement; 2.2 Description of Methodology; 2.2.1 Phase A. Data Preparation in Dynamic Environments; 2.2.2 Phase B. Decision Evaluation Process with Feedback; 2.3 Example in Practice; 2.3.1 Background about Landing Site Selection; 2.3.2 Phase A. Data Preparation in Dynamic Environments; 2.4 Future Research Directions; Bibliography
Chapter 3: Decision Making under Uncertainty by Possibilistic Linear Programming Problems3.1 Introduction; 3.2 Fuzzy Decisions in Possibility Linear Programming Problems; 3.2.1 Triangular Possibility Distributions of Fuzzy Decision Variables; 3.3 Numerical Examples; 3.4 Conclusions; Bibliography; Chapter 4: Intelligent Decision Making in Training Based on Virtual Reality; 4.1 Training Aspects; 4.2 Virtual Reality; 4.2.1 Interaction Devices; 4.2.2 Haptic Devices; 4.3 Training in Virtual Reality Systems; 4.4 Collecting and Using Data from VR Simulators; 4.4.1 Online and Offline Evaluation
4.5 Evaluation Based on Expert Systems4.5.1 Using Classical Logic; 4.5.2 Using Fuzzy Logic; 4.5.3 Combining Expert Systems; 4.6 Evaluation Based on Mixture Models; 4.6.1 Gaussian Mixture Models; 4.6.2 Fuzzy Gaussian Mixture Models; 4.6.3 Sequential Methods; 4.7 EvaluationMethods Based on Hidden Markov Models; 4.7.1 Discrete Hidden Markov Models (DHMM); 4.7.2 Continuous Hidden Markov Models; 4.7.3 Comparison of Hidden Markov Models; 4.7.4 Fuzzy Hidden Markov Models; 4.8 EvaluationMethods Based on Bayesian Models; 4.8.1 Maximum Likelihood; 4.8.2 Fuzzy Bayes Rule; 4.8.3 Naive Bayes
4.8.4 Quantitative and Qualitative Evaluation Based on Naive Bayes4.8.5 Bayesian Networks; 4.9 Considerations About EvaluationMethods; 4.10 Future Trends and Conclusions; Bibliography; Chapter 5: A Many-Valued Temporal Logic and Reasoning Framework for Decision Making; 5.1 Introduction; 5.1.1 Problem Description: a Realistic Scenario; 5.1.2 Relevant Works; 5.2 Many-valued Temporal Propositional Logic Systems; 5.3 Practical Many-valued Temporal Reasoning; 5.3.1 Forward Reasoning Algorithm; 5.3.2 Backward Reasoning Algorithm; 5.4 Scenarios; 5.5 Discussions; 5.6 Conclusions; Acknowledgment
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