Artificial Intelligence and Expert Systems for Engineers 1st edition by Krishnamoorthy, Rajeev ISBN 0849391253  9780849391255

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ISBN 10: 0849391253
ISBN 13: 9780849391255
Author: Krishnamoorthy, Rajeev

This book provides a comprehensive presentation of artificial intelligence (AI) methodologies and tools valuable for solving a wide spectrum of engineering problems. What’s more, it offers these AI tools on an accompanying disk with easy-to-use software.
Artificial Intelligence and Expert Systems for Engineers details the AI-based methodologies known as: Knowledge-Based Expert Systems (KBES); Design Synthesis; Design Critiquing; and Case-Based Reasoning. KBES are the most popular AI-based tools and have been successfully applied to planning, diagnosis, classification, monitoring, and design problems. Case studies are provided with problems in engineering design for better understanding of the problem-solving models using the four methodologies in an integrated software environment.
Throughout the book, examples are given so that students and engineers can acquire skills in the use of AI-based methodologies for application to practical problems ranging from diagnosis to planning, design, and construction and manufacturing in various disciplines of engineering.
Artificial Intelligence and Expert Systems for Engineers is a must-have reference for students, teachers, research scholars, and professionals working in the area of civil engineering design in particular and engineering design in general.

Artificial Intelligence and Expert Systems for Engineers 1st Table of contents:

Preface

• Introduction to AI and Expert Systems in Engineering
• Purpose of the Book
• How This Book Is Organized
• Audience and Prerequisites

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Part I: Introduction to Artificial Intelligence and Expert Systems

1. Introduction to Artificial Intelligence

   • Definition and Scope of Artificial Intelligence
   • History and Evolution of AI
   • Key Concepts and Terminology in AI
   • Types of AI: Narrow AI vs. General AI
   • AI Applications in Engineering and Industry

2. Fundamentals of Expert Systems

   • What is an Expert System?
   • Components of an Expert System
   • Knowledge Representation: Rules, Frames, and Ontologies
   • Inference Mechanisms: Forward and Backward Chaining
   • Knowledge Acquisition and Validation
   • Benefits and Limitations of Expert Systems

3. AI Techniques for Engineers

   • Machine Learning and Its Applications in Engineering
   • Rule-Based Systems and Logic Programming
   • Fuzzy Logic in Engineering Systems
   • Neural Networks: Basics and Engineering Applications
   • Genetic Algorithms and Evolutionary Computing

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Part II: Knowledge Representation and Reasoning

4. Knowledge Representation in Engineering

   • The Role of Knowledge Representation in Expert Systems
   • Types of Knowledge: Declarative vs. Procedural
   • Semantic Networks and Frames
   • Representing Complex Engineering Knowledge
   • Ontologies in Engineering Applications

5. Reasoning with Expert Systems

   • Logical Reasoning in Expert Systems
   • Forward and Backward Chaining Algorithms
   • Rule-Based Inference and Decision Trees
   • Uncertainty and Approximate Reasoning in Expert Systems
   • Probabilistic Reasoning and Bayesian Networks

6. Fuzzy Logic Systems

   • Introduction to Fuzzy Logic
   • Fuzzy Sets and Membership Functions
   • Fuzzy Inference Systems
   • Applications of Fuzzy Logic in Engineering
   • Design of Fuzzy Controllers and Systems

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Part III: Machine Learning and Neural Networks in Engineering

7. Introduction to Machine Learning

   • What is Machine Learning?
   • Supervised Learning: Regression and Classification
   • Unsupervised Learning: Clustering and Dimensionality Reduction
   • Reinforcement Learning and Its Engineering Applications
   • Performance Metrics and Model Evaluation

8. Neural Networks and Deep Learning

   • Basics of Neural Networks: Perceptron, MLP, and Backpropagation
   • Convolutional Neural Networks (CNNs) for Image Processing
   • Recurrent Neural Networks (RNNs) and Time-Series Data
   • Deep Learning Architectures and Applications in Engineering
   • Training Neural Networks: Challenges and Techniques

9. Applications of Machine Learning and Neural Networks in Engineering

   • Predictive Maintenance in Manufacturing Systems
   • Process Control and Optimization Using ML
   • Fault Detection and Diagnostics in Complex Systems
   • Autonomous Systems: Robotics and Vehicles
   • Predictive Modeling and Simulation

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Part IV: Expert Systems in Engineering Practice

10. Design and Development of Expert Systems

    • Expert System Design Process
    • Knowledge Acquisition: Methods and Challenges
    • Building an Expert System: Tools and Platforms
    • Prototyping and Iterative Development
    • Testing and Validation of Expert Systems

11. Applications of Expert Systems in Engineering

    • Expert Systems in Mechanical Engineering: Design, Analysis, and Manufacturing
    • Expert Systems for Electrical and Electronics Engineering
    • Civil Engineering: Structural Health Monitoring and Design
    • Chemical Engineering: Process Control and Safety Systems
    • AI in Environmental Engineering and Sustainability

12. Hybrid Expert Systems

    • Combining Expert Systems with AI Techniques
    • Expert Systems and Neural Networks
    • Fuzzy Expert Systems: Applications and Case Studies
    • Expert Systems in Intelligent Decision Support Systems
    • Applications of Hybrid Systems in Engineering

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Part V: Advanced Topics and Future Directions

13. AI for Smart Engineering Systems

    • The Concept of Smart Systems and Smart Engineering
    • Internet of Things (IoT) and AI Integration
    • AI in Predictive Maintenance and Monitoring Systems
    • Industry 4.0: AI-Powered Manufacturing and Automation
    • Smart Grids and AI for Energy Systems

14. Ethics and Challenges in AI and Expert Systems

    • Ethical Issues in AI and Expert Systems
    • Ensuring Fairness and Transparency in AI Systems
    • The Role of Engineers in Developing Ethical AI
    • Safety and Security Concerns in AI Systems
    • Regulatory and Legal Frameworks for AI in Engineering

15. The Future of AI and Expert Systems in Engineering

    • Emerging Trends in AI Technologies
    • AI and Expert Systems in the Era of Big Data and Cloud Computing
    • The Role of AI in Sustainable Engineering Practices
    • Next-Generation Expert Systems: Autonomous Design and Self-Optimization
    • Challenges and Opportunities for Engineers in AI Integration

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Conclusion

• Summary of Key Concepts
• Future Prospects for AI and Expert Systems in Engineering
• The Role of Engineers in AI and Expert Systems Development

References

• Key Publications and Further Reading

Index

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