Soft Computing Techniques for Intelligent Control
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Follow on Instagram for UpdatesSoft Computing Techniques for Intelligent Control Syllabus — Unit-wise Content
The official GTU curriculum for Soft Computing Techniques for Intelligent Control (subject code 2712911) as prescribed for PE · Semester 1 divides the course into 5 units, covering 28 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 (2712911)Unit 1 Introduction
- Introduction to Soft Computing
- Importance of Soft Computing
- Main Components of Soft Computing
- Fuzzy Logic
- Artificial Neural Networks
- Introduction to Evolutionary Algorithms
- Hybrid Intelligent Systems
Unit 2 Artificial Neural Networks
- Concept of Artificial Neural Networks and its basic mathematical model
- McCulloch-Pitts Neuron model
- simple perception
- Adeline and Madeline
- Feed-forward Multilayer Perception
- Learning and Training the neural network .Hopfield network
- Self-organizing network and recurrent network
- Neural Network controller Application
Unit 3 Fuzzy Logic System
- Introduction of Fuzzy Logic
- fuzzy sets
- Basic fuzzy set operation and Properties of fuzzy sets
- Introduction to fuzzy logic modeling and control- Fuzzification- inferencing and de-fuzzification-Fuzzy knowledge and rule bases-Fuzzy modeling and control schemes for nonlinear systems
- Self-organizing fuzzy logic control- Fuzzy logic control for nonlinear time delay system
Unit 4 Genetic algorithm
- History of Genetics
- Basic concept of Genetic algorithm and detail algorithmic steps
- adjustment of free parameters
- Solution of typical control problems using genetic algorithm
- Concept on some other search techniques like tabu search and and-colony search techniques for solving optimization problems
Unit 5 Applications
- Application of GA to power system optimization problem
- Identification and control of linear and nonlinear dynamic systems using Matlab-Neural Network toolbox
- Stability analysis of Neural Network interconnection systems- Implementation of fuzzy logic controller using Matlab- fuzzy logic toolbox-Stability analysis of fuzzy control systems
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