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Soft Computing

BI 1429

New

SOFT COMPUTING

Author: Anjali Sardesai

According to new reveised syllabus w.e.f. 2012 for Masters of Computer Science (M.Sc.) 

 

BI: 1429

ISBN: 9789350161470

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Rs.234

Rs.260

-10%

1. Classical Sets and Fuzzy Sets and Fuzzy Relations 

1. Introduction  

2. Classical Sets

2. Membership Functions

1. Introduction  

2. Features of Membership Functions

3. Standard Forms and Boundaries   

4. Inference

3. Fuzzy to Crisp Conversions

1. Introduction  

2. Lambda Cuts for Fuzzy Sets

3. Fuzzy Relations     

4. Zadeh’s Extension Principle     

5. Fuzzy Arithmetic    

6. Defuzzification

4. Classical Logic and Fuzzy Logic 

1. Introduction  

2. Classical Predicate Logic 

3. Fuzzy Logic   

4. Approximate Reasoning and Fuzzy Implication

5.  Fuzzy Rule Based System  

1.  Introduction 

2. Linguistic Hedges   

3. Fuzzy Inference System with MATLAB    

4. Fuzzy Inference System using MATLAB 

4. Other Miscellaneous Applications

6. Applications of Fuzzy Logic     

1. Introduction  

2. How Fuzzy Logic is applied in Home Appliances?    

3. General Fuzzy Logic Controllers 

4. Basic Medical Diagnostic Systems

5. Weather Forecasting 

6. Fuzzy Inference System

7. Introduction to Neural Networks 

1. Introduction  

2. Advent of Modern Neuroscience   

3. Classical AI and Neural Networks

4. Hybrid Intelligent Systems

5. Biological Neurons

8. Artificial Neurons, Neural Networks and Architectures   

1. Introduction  

2. Neuron Abstraction  

3. Architectures: Feedforward and Feedback     

4. Salient Properties and Application Domains  

5. Mc Culloch Pitts Neuron

9. Perceptrons and LMS 

1. Introduction  

2. Learning and Memory 

3. From Synopses to Behaviour: The Case of Aplysia   

4. Learning Algorithms 

5. Error Correction and Gradient Descent Rules 

6. The Learning Objectives for TLNs

7. Pattern Space and Weight Space  

8. Pereptron Learning Algorithm    

9. Perceptron Convergence Theorem  

10. Perceptron Learning and Nonseparable Sets  

11. a -Least Mean Square Learning  

12. MSE Error Surface and its Geometry   

13. Steepest Descent Search with Exact Gradient Information

14. m -LMS Approximate Gradient Descent  

15. Back Propagation Learning Algorithm  

16. Neural Network using MATLAB

10. Applications of Neural Networks

1. Introduction  

2. Pattern Recognition 

3. Neural Networks in Medicine     

4. Neural Networks in Business     

5. Neural Networks in Character Recognition    

6. Image Compression

11.Genetic Algorithms (GA)   

1. Introduction  

2. Introduction to GA  

3. Robustness of Traditional Optimization and Search Methods     

4. How are Genetic Algorithms Different From Traditional Methods?

5. Basic Terminologies in GA 

12.Applications of Genetic Algorithm     

1. Introduction  

2. GA Based Clustering Algorithm   

3. Image Processing and Pattern Recognition    

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