Instabooks AI (AI Author)
Soft Computing Essentials
With Numerical Examples for BTech Students
Premium AI Book (PDF/ePub) - 200+ pages
Why Choose This Book?
- Engaging explanations of fuzzy logic, neural networks, and genetic algorithms supplemented with BTech level numericals.
- Practical applications that illustrate the power and flexibility of soft computing techniques in various fields.
- Advanced discussions on the integration of soft computing in modern technology-led industries.
Table of Contents
1. Introduction to Soft Computing- Defining Soft Computing in Modern Tech
- Comparison with Traditional Computing Methods
- Scope and Applications in Engineering
2. Fuzzy Logic: Fundamentals and Machinery
- Understanding Fuzzy Sets and Systems
- Building Fuzzy Models with Numericals
- Real-world Applications of Fuzzy Logic
3. Neural Networks: Architectures and Learning
- Basics of Artificial Neural Networks
- Training Networks through Examples
- Neural Networks in Industry and Research
4. Genetic Algorithms for Optimization
- Principles of Evolutionary Computation
- Designing and Running Genetic Algorithms
- Case Studies: Genetic Algorithms in Action
5. Soft Computing Integration Techniques
- Combining Soft Computing Paradigms
- Fusion of Fuzzy Logic and Neural Networks
- Hybridizing Genetic Algorithms with Machine Learning
6. Support Vector Machines and Kernel Methods
- Grasping the SVM Framework
- Numerical Implementation of Kernel Methods
- Comparative Advantages in Classification Tasks
7. Computational Intelligence in Control Systems
- Adaptive Control with Computational Techniques
- Neural Control Systems and their Dynamics
- Fuzzy Logic Controllers: Design and Application
8. Evolutionary Computation and Swarm Intelligence
- Exploring Agent-based Modeling
- Practical Numericals on Swarm Optimization
- Application of Swarm Intelligence in Robotics
9. Probabilistic Methods in Soft Computing
- Bayesian Networks and Decision Processes
- Numerical Problems: Probabilistic Reasoning
- Applications in Machine Learning and AI
10. Machine Learning Techniques
- Overview of Learning Algorithms
- Numerical Analysis of Machine Learning Models
- Trends and Future Directions
11. Advanced Topics in Soft Computing
- Emergent Paradigms in Computational Models
- Numerical Case Studies: Cutting-edge Applications
- Integrating Soft Computing into Modern Technologies
12. Project-based Learning in Soft Computing
- Designing Projects with a Soft Computing Focus
- Collaborative Tools and Techniques
- Case Studies and Real-World Problem Solving
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