Soft Computing
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Follow on Instagram for UpdatesSoft Computing Syllabus — Unit-wise Content
The official GTU curriculum for Soft Computing (subject code BE05043021) as prescribed for AI & DS · Semester 5 divides the course into 7 units, covering 53 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 (BE05043021)Unit 1 Introduction to Soft Computing
- Hard Computing: features and examples
- Soft Computing: features and examples
- Hybrid Computing and its significance
- distinction between hard and soft computing
- components of soft computing
- role of uncertainty and imprecision in real-world problem solving
Unit 2 Optimization
- Introduction to optimization
- classification of optimization problems
- principle of optimization
- duality principle
- traditional methods: Exhaustive Search Method
- Random Walk Method and Steepest Descent Method
- drawbacks of traditional methods
- overview of Simulated Annealing and Particle Swarm Optimization
- basic comparison of PSO and GA
Unit 3 Genetic Algorithms
- Working cycle of Genetic Algorithm
- binary-coded GA
- crossover and mutation
- schema theorem
- limitations of binary-coded GA
- GA parameter setting
- penalty function approach for constraint handling
- advantages and disadvantages of GA
- real-coded GA with BLX-alpha and SBX crossover
- mutation operators
Unit 4 Introduction to Fuzzy Sets
- Crisp sets: notation
- operations and properties
- fuzzy sets: representation and difference from crisp sets
- normality and convexity
- fuzzy set operations
- fuzzy relations and composition
- properties of fuzzy sets
- measures of fuzziness and inaccuracy
Unit 5 Neural Networks
- Biological neuron and artificial neuron
- layer and multi-layer architectures
- static versus dynamic neural networks
- incremental and batch training
- Multi-Layer Feed-Forward Neural Network (MLFFNN): forward calculation
- back-propagation learning
- design steps
- advantages and limitations
- simple numerical understanding
Unit 6 Hybrid Soft Computing
- Fuzzy-Genetic Algorithm: concept
- structure and working principle
- Neuro- Fuzzy System based on Mamdani approach
- basic tuning using back- propagation and Genetic Algorithm
Unit 7 Applications of Soft Computing
- Applications in intelligent autonomous robots
- environment information collection
- motion planning and motor control
- applications in data analysis
- classification and predictive modelling
- illustrative case studies in pattern recognition
- diagnosis and engineering decision-making
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