Advanced Operating System
According to New Revised CBCS Syllabus w.e.f. 2019-20
M.Sc. (Computer Science)
Semesters-II
A Text book of
Advanced Operating System
Author: Suvarna Jagtap
ISBN: 978-81-944620-6-4
Price: 265/-
According to New Revised CBCS Syllabus w.e.f. 2019-20
A Text book of
Author: Dr. Anjali Sardesai
ISBN: 978-81-945104-3-7
Price: 465/-
Contents
1. Introduction to Soft Computing
1. Neural Networks
1.1 Advantages of Neural Networks
1.2 Applications of Neural Network
1.3 Neural Networks in Character Recognition
1.3 Scope of Neural Network
2. Fuzzy Logic: Definition, Applications
3. Genetic Algorithms: Definition, Applications
2. Neural Network
1. Introduction
1.1 Historical Background of Neural Network
2. Fundamental Concepts
2.1 Hybrid Intelligent Systems
2.2 Biological Neurons
2.3 Brain Vs Computers
3. Artificial Neurons, Neural Networks and Architectures
3.1 Neuron Abstraction
3.2 Neuron Activation 15
3.3 Neuron Signal Functions
3.4 Mathematical Preliminaries
3.5 Neural Networks
3.6 Network Architecture
3.7 Salient Properties of Neural Networks
4. Geometry of Binary Threshold Neurons and their Networks
4.1 Pattern Recognition and Data Classification
4.2 Convex Sets, Convex Hulls and Linear Separability
4.3 Space of Boolean Functions
4.4 Binary Neurons are a Pattern Dichotomizers
4.5 Non-linearly Separable Problems
4.6 Capacity of a Simple Threshold Logic Neuron
4.7 Revisiting XOR Problem
4.8 Multilayer Networks
4.9 How Many Hidden Nodes are enough?
5. Learning and Memory
5.1 An Anecodatal Introduction
5.2 The Behavioral Approach to Learning
5.3 The Molecular Problem of Memory
5.4 Learning Algorithms
5.5 Error Correction and Gradient Descent Rules
5.6 The Learning Objectives of TLNs
5.7 Learning Objective
5.8 Pattern Space and Weight Space
6. Linear Separability, Hebb Network, Perceptron Network
6.1 Linear Separability
6.2 Hebb Network
6.3 Perceptron Network
6.4 Learning (Training) Process
6.5 Design in Weight Space
6.6 ?-Least Mean Square Learning
6.7 ?–LMS Works with Normalized Training Patterns
7. MSE Error Surface and its Geometry
7.1 Steepest Descent Search with Exact Gradient Information
7.2 ?-LMS Approximate Gradient Descent
7.3 ?-LMS Algorithm: Convergence in the Mean
8. Application of LMS to Noise Cancellation
3. Fuzzy Set Theory
1. Brief Review of Conventional Set Theory
1.1 Definition of Set
1.2 Set Terminologies
1.3 Operations of Classical Sets
1.4 Properties of Classical Sets
1.5 Mapping of Classical Sets to Functions
2. Fuzzy Sets
2.1 Fuzzy Set as a Whole
2.2 Fuzzy Set Terminologies
2.3 Fuzzy Set Operations
2.4 Properties of Fuzzy Sets
3. Cartesian Product
4. Crisp Relations
5. Fuzzy Relations
6. Tolerance and Equivalence Relations
7. Fuzzy Tolerance and Equivalence Relations
7.1 Fuzzy Equivalence Relation
7.2 Fuzzy Tolerance Relation
8. Value Assignments
9. Membership Functions
9.1 Features of Membership Functions
10. Various Forms
10.1 Standard Forms and Boundaries
11. Fuzzification
11.1 Fuzzification Process
12. Defuzzification to Crisp Set
13. ?-Cuts for Fuzzy Relations
14. Defuzzification to Scalars
14.1 Defuzzification Methods
15. Fuzzy Logic 102
15.1 What is Fuzzy Logic?
15.2 Fuzzy Propositions
15.3 Logical Connectives for Fuzzy Logic
16. Approximate Reasoning
17. Other Forms of Implication Operation
18. Natural Language
19. Linguistic Hedges
19.1 Linguistic Variables
20. Fuzzy (Rule Based) System
20.1 Graphical Technique of Interference
20.2 Fuzzy Inference Process
20.3 Structure of Fuzzy Inference System
21. Graphical Techniques of Inference
21.1 Comparison between Mamdani and Sugeno model
22. Membership Value Assignments
23. Inference
4. Genetic Algorithms
1. Introduction
1.1 History of Genetic Algorithms
2. What is Genetic Algorithm?
3. Why Genetic Algorithm?
3.1 Application of GA
4. Robustance of Traditional Optimization and Search Methods
4.1 Traditional Optimization or Search Methods
5. Goals of Optimization
6. How are Genetic Algorithms Different from Traditional Methods?
6.1 Basic Terminologies in GA
7. Simple GA
7.1 Encoding Methods in Genetic Algorithm
7.2 Selection
7.3 Operators in Genetic Algorithm
7.4 Search Termination / Termination Condition
8. Genetic Algorithms At Work – A Simulation By Hand
9. Grist for the Search Mill – Important Similarities
10. Similarity Templates (Schemata)
11. Learning the Lingo