Soft Computing Techniques
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Follow on Instagram for UpdatesSoft Computing Techniques Syllabus — Unit-wise Content
The official GTU curriculum for Soft Computing Techniques (subject code ME02069151) as prescribed for C(E · Semester 2 divides the course into 4 units, covering 31 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 (ME02069151)Unit 1 Introduction to Soft Computing
- Concept of computing systems
- "Soft" computing versus "Hard" computing
- Characteristics of Soft computing
- Some applications of soft computing techniques
Unit 2 Genetic Algorithms
- Goals of optimization
- comparison with traditional methods
- Terminology in GA – strings
- parameter string
- data structures
- coding fitness function
Unit 3 Fuzzy Logic
- Concepts of uncertainty and imprecision
- properties and operations on classical sets & fuzzy sets
- classical & fuzzy relations
- membership functions
- fuzzy logic
- fuzzy rule-based systems
- fuzzy propositions, and applications
- Program Name: Master of Engineering Level: PG Branch: Civil Engineering (Transportation Engineering) Subject Code: ME02069151 Subject Name: Soft Computing Techniques
Unit 4 Artificial Neural Networks
- Basics of ANN: Models of a Neuron
- Multi-Layer Feed Forward Network (MLFFN)
- Radial Basis Function Network (RBFN)
- Recurring Neural Network (RNN)
- learning processes: supervised and unsupervised learning. error-correction learning
- Hebbian learning
- single layer perceptrons
- multilayer perceptrons
- least mean square algorithm
- back propagation algorithm applications. 12 30 6
- Hybrid Systems: Fuzzy Neural systems
- Genetic Fuzzy systems
- Genetic Neural systems
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