Soft Computing
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Soft Computing Syllabus — Unit-wise Content
The official GTU curriculum for Soft Computing (subject code 3172009) as prescribed for Robotics · Semester 7 divides the course into 6 units, covering 35 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3172009)Unit 1 Introduction to soft computing
- Importance of Soft Computing
- Main Components of Soft Computing
- Fuzzy Logic
- Artificial Neural Networks
- Support Vector machine
- Evolutionary Algorithms
- Hybrid Intelligent Systems
Unit 2 Fuzzy Logic Systems
- Introduction to Fuzzy logic
- classical sets vs fuzzy sets
- Membership functions and its features
- Properties and operations on Fuzzy sets
- classical relations vs Fuzzy relation
- Operations of Fuzzy relation
- Fuzzy rule base and approximate reasoning
- Fuzzy Inference Systems
- Design a fuzzy logic controller: Mamdani & Sugeno Architecture
Unit 3 Neural Network Systems
- Introduction to Artificial Neural Network
- Biological neurons vs artificial neural network
- Neuron models: McCulloch-Pitts Neuron
- Hebb Network
- Learning in neural networks: Supervised Learning Network – Perceptron
- Back propagation network
- Unsupervised Learning Network – Self organizing Map, Learning Vector Quantization
- Adaptive resonance theory
Unit 4 Genetic Algorithms
- Concept of "Genetics" and "Evolution" and its application to probabilistic search techniques
- Basic GA framework and different GA architectures
- GA operators: Encoding
- Mutation, etc
- Solving single-objective optimization problems using GAs
Unit 5 Hybrid Systems and applications of soft computing
- Fuzzy logic and neural network systems in Control System
- Hybrid Systems - Fuzzy Neural
- Genetic Fuzzy
- Genetic Neural
- Applications in automation
- robotics and Machine vision
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