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 ME01095041) as prescribed for AIAD · Semester 1 divides the course into 6 units, covering 55 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 (ME01095041)Unit 1 Soft Computing Basics
- Conventional AI to Computational Intelligence
- Soft Computing Constituents and Applications
- Soft Computing vs
- Hard Computing Data Clustering Algorithms: K-Means
- Fuzzy C-Means
- Mountain Clustering
- Subtractive Clustering
Unit 2 Neural Networks
- Taxonomy of neural net, characteristics and applications of ANN, McCulloch Pitt model
- different activation functions Supervised Learning algorithms- Perceptron (Single Layer
- Multi layer)
- Linear separability
- RBF networks
- Widrow Hoff
- learning rule
- Delta learning rule
- Back Propagation algorithm Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095041 Subject Name: Soft Computing Un-Supervised Learning algorithms- Hebbian Learning
- Winner take all, Self Organizing Maps Adaptive Resonance Theory: Architecture
- Implementation and training
- Associative Memory
- Convolutional neural Networks (CNN)
- Recurrent neural Networks (RNN)
Unit 3 Fuzzy Logic
- Fuzzy set theory
- Introduction & features of membership functions
- Fuzzy rule base system : fuzzy propositions
- decomposition & aggregation of fuzzy rules
- fuzzy reasoning
- fuzzy quantifier
- fuzzy inference systems
- Mamdani Fuzzy Models – Sugeno Fuzzy Models
- Adaptive Neuro-Fuzzy Inference Systems Architecture
- Application using fuzzy system - Air Conditioner Controller
Unit 4 Introduction to Non-traditional Metaheuristic Optimization Techniques
- Random Optimization
- Simulated Annealing
- Tabu Search
- Ant Colony Optimization
- Particle Swarm Optimization
- Harmony Search
- Memetic Algorithms
- Other Evolutionary Algorithms such as Firefly Algorithm
- Bee Algorithm
- Shuffled Frog Leap algorithm
- Bat algorithm etc
Unit 5 Genetic algorithm
- Encoding styles – Binary
- fitness function
- reproduction Genetic modeling - Inheritance operator
- cross over
- mutation operator
- Generational Cycle
- Convergence of GA, Genetic Modelling
- Applications & advances in GA, Differences & similarities between GA & other traditional methods
Unit 6 Hybrid Systems
- Hybrid systems
- GA based BPNN (Weight determination
- Neuro Fuzzy Systems—Fuzzy BPNN--fuzzy Neuron
- Fuzzy Logic controlled G.A
- Fuzzy Associative Memories
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