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Behnam Malakooti, Ph.D., P.E.
 

Artificial Neural Networks and Expert Systems: Multiple Objective Optimization

Behnam Malakooti

Brief Table Of Contents Detailed Table Of Contents

  • Part I. Modeling and Solving Multiple Criteria Decision Making Problems by Feedforward Artificial Neural Networks

    • Chapter 1 Adaptive Feedforward Artificial Neural Networks for Solving Multiple Criteria Decision Making Problems
      • An Adaptive Feedforward Artificial Neural Network with Application to Multiple Criteria Decision Making (16)
      • 1. Introduction
      • 2. An Adaptive Feedforward Artificial Neural Network Algorithm
        • 2.1. Some Notation and Basic Concepts
        • 2.2. Adaptive Strategy Algorithm to Find the AF-ANN Topology
      • 3. The Basic Properties of Multiple Criteria Decision Making with Adaptive Feedforward Artificial Neural Networks
        • 3.1. Basic Concepts Related to Multiple Criteria Decision Making
        • 3.2. Concave or Convex AF-ANNs
      • 4. An Approach to Training Adaptive Feedforward Artificial Neural Networks and Ranking Discrete Multiple Criteria Alternatives
      • 5. Computational Experiments with Adaptive Feedforward Artificial Neural Networks for Discrete Multiple Criteria Decision Making Problems
      • 6. Conclusions and Discussion
      • 7. Appendix A. Proofs for Propositions
      • 8. Appendix B. A Flexible Learning Algorithm to Assess Parameters of a Given AF-ANN Topology
      • 9. References
    • Chapter 2 Optimal Learning and Design of Feedforward Artificial Neural Networks
      • Characterization of Training Errors in Supervised Learning Using Gradient-Based Learning Rules (20)
      • 1. Abstract
      • 2. Introduction
      • 3. Concept Formalization
      • 4. Steady-State, Local Minimum, and Global Minimum Errors
      • 5. Global Minimum and Absolute Minimum Errors
      • 6. Absolute Minimum Errors and General Neural Networks
      • 7. Absolute Minimum Errors and Semilinear Feedforward Neural Networks
      • 8. Absolute Minimum Errors and Higher-Order Feedforward Neural Networks
      • 9. Absolute Minimum Errors and Recurrent Neural Networks
      • 10. Conclusions
      • 11. References
    • Chapter 3 Approximating Multiple Criteria Polynomial Functions by Feedforward Artificial Neural Networks
      • Approximating Polynomial Functions by Feedforward Artificial Neural Networks: Capacity, Analysis, and Design (11)
      • 1. Abstract
      • 2. Introduction
      • 3. Capacity of Feedforward Artificial Neural Networks in Approximating Polynomials
      • 4. A Method to Realize Feedforward Artificial Neural Networks in Approximating Polynomial Functions
      • 5. Training Algorithm and Computational Examples
      • 6. Conclusions
      • 7. References
    • Chapter 4 Adaptive Design of Feedforward Artificial Neural Networks
      • An Adaptive Strategy to Design the Structure of Feedforward Neural Nets (19)
      • 1. Abstract
      • 2. Introduction
      • 3. Matrix Notation for Feedforward Artificial Neural Networks
      • 4. Theory of Adaptive Strategy
      • 5. One Dimensional Search
      • 6. Adaptive Strategy Algorithm
      • 7. Illustrative Examples
      • 8. Conclusion
      • 9. References
    • Chapter 5 Overview of Feedforward Artificial Neural Networks for Solving Multiple Criteria Decision Making Problems
      • A Feedforward Neural Network for Multiple Criteria Decision Making (23)
      • 1. Introduction
      • 2. Problem Formulation
      • 3. Artificial Neural Network Constructive Specifications
      • 4. Artificial Neural Network Model Configuration
      • 5. Computer Simulation
      • 6. Conclusions
      • 7. References
      • 8. Appendix
    • Chapter 6 Recursive Artificial Neural Networks for Solving Adaptive Multiple Criteria Decision Making Problems
      • A Recursive Artificial Neural Network for Solving Adaptive Multiple Criteria Problems (24)
      • 1. Introduction
      • 2. Definitions and Notations of Adaptive Multiple Criteria Decision Making Problems
        • 2.1. MCDM Notations
        • 2.2. FANN Notation
        • 2.3. FANN Models for Adaptive MCDM Problems
      • 3. Methodology and Properties
        • 3.1. An Adaptive Training Algorithm for Training FANN
        • 3.2. Adaptive MCDM Problems
      • 4. An Example with some Computational Experiments
      • 5. Conclusions
      • 6. References
      • 7. Appendix 1: An Algorithm for Training a FANN
      • 8. Appendix 2: Modification 1 of Matrix Ap: When the Weights Change
      • 9. Appendix 3: Modification 2 of Matrix Ap: When the New Pattern is Available

  • Part II. Applications of Multiple Criteria Decision Making based on Artificial Neural Networks

    • Chapter 7 Interactive Machine Set-up Optimization using Artificial Neural Networks
      • An Interactive Artificial Neural Network approach for machine set-up optimization (9)
      • 1. Introduction
      • 2. Basic Notations, and Review of Methods for Machine Setup
      • 3. Concepts of Artificial Neural Networks
        • 3.1. Feedforward Artificial Neutral Network
      • 4. Artificial Neural Network Method for Machine Setup
        • 4.1. Problem Formulation
        • 4.2. Summary of Developed Algorithm
      • 5. Example
      • 6. Conclusions
      • 7. References
    • Chapter 8 Sensor-based Machine Monitoring and Supervision using Regression and Adaptive Feedforward Artificial Neural Networks
      • In-Process Regressions and Adaptive Neural Networks for Monitoring and Supervising Machining Operations (15)
      • 1. Introduction
      • 2. Problem Formulation
        • 2.1. Monitoring Problem
        • 2.2. Supervising Problem
      • 3. In-Process Regressions for Monitoring Tool Condition
        • 3.1. In-process Regression Formulas
        • 3.2. Regression 1 (a Piecewise Regression Model)
        • 3.3. Regression 2 (Updating Regression Parameters Considering Different Setups)
        • 3.4. Regression 3 (Predicting Tq(1) and Vb(1) Considering Different Setups)
        • 3.5. The Monitoring of Tool Failure
        • 3.6. An Example for the Monitoring Problems
      • 4. Adaptive Feedforward Artificial Neural Network for Supervision of Machining Operations
        • 4.1. An Approach to Training the AF-ANN and Ranking Alternatives
        • 4.2. AF-ANN for Supervising a Machining Operation
        • 4.3. An Example for the Supervising Problem
      • 5. Implementation of the Monitoring and Supervising System
        • 5.1. Monitoring and Supervising Machining Operations Algorithm
      • 6. Conclusions
      • 7. References
      • 8. Appendix 1. Derivation of In-process Regression for the Piecewise Model
    • Chapter 9 Solving Clustering and Group Technology Problems by Unsupervised Artificial Neural Networks
      • A Variable-Parameter Unsupervised Learning Clustering Neural for Machine Part Group Formation (14)
      • 1. Introduction
      • 2. Basic Notations, Definitions, and Review of Methods for Machine-Part Group Formation
      • 3. Self-Organizing Neural Network
      • 4. A Variable-Parameter Unsupervised Learning Algorithm
      • 5. Some Experimental Results
      • 6. Conclusion
      • 7. References

  • Part III. Multiple Objective Expert Systems and Applications

    • Chapter 10 Multiple Objective Expert Systems for Solving Assembly Line Balancing Problems
      • An Expert System for Solving Multi-objective Assembly Line Balancing Problems (12)
      • 1. Introduction
      • 2. The Multiple Objective Assembly Line Balancing Problem and Selected Procedures
      • 3. Design of the Expert System for the Multiple Objective Assembly Line Balancing Problem
      • 4. The Operation of the Expert System for the Multiple Objective Assembly Line Balancing Problem
      • 5. A Session with the Expert System for the Multiple Objective Assembly Line Balancing Problem
      • 6. Conclusions
      • 7. References
    • Chapter 11 Multiple Objective Expert Systems for Solving Facility Layout Problems
      • An Expert System Using Priorities for Solving Multiple Criteria Facility Layout Problems (37)
      • 1. Introduction
      • 2. Layout Construction by Expert Systems
        • 2.1. Database
        • 2.2. Knowledge Base
        • 2.3. Priority Base
      • 3. The Mechanism of the Inference Engine
      • 4. Some Experiments with Computer Package
      • 5. An Example
      • 6. Conclusions
      • 7. References