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AKTU Application of Soft Computing (BCS056H) Syllabus PDF – B.Tech 3rd Year
Application of Soft Computing BCS056H
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Official AKTU syllabus of Application of Soft Computing (BCS056H) for B.Tech 3rd Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Application of Soft Computing
Download PDFBCS056H · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course , the student will be able to:
| CO 1 | Recognize the feasibility of applying a soft computing methodology for a particular problem | K , K 2 4 |
| CO 2 | Know the concepts and techniques of soft computing and foster their abilities in designing and implementing soft computing based solutions for real-world and engineering problems. | K , K 4 6 |
| CO 3 | Apply neural networks to pattern classification and regression problems and compare solutions by various soft computing approaches for a given problem. | K , K 3 5 |
| CO 4 | Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems | K , K 3 4 |
| CO 5 | Apply genetic algorithms to combinatorial optimization problems | K 3 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Neural Networks-I (Introduction & Architecture) : Neuron, Nerve structure and synapse, Artificial Neuron and its model, activation functions, Neural network architecture: single layer and multilayer feed forward networks, recurrent networks. Various learning techniques; perception and convergence rule, Auto-associative and hetro-associative memory. | 08 |
| II | Neural Networks-II (Back propogation networks): Architecture: perceptron model, solution, single layer artificial neural network, multilayer perception model; back propogation learning methods, effect of learning rule co-efficient ;back propagation algorithm, factors affecting backpropagation training, applications. | 08 |
| III | Fuzzy Logic-I (Introduction): Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set theory and operations, Properties of fuzzy sets, Fuzzy and Crisp relations, Fuzzy to Crisp conversion. | 08 |
| IV | Fuzzy Logic –II (Fuzzy Membership, Rules) : Membership functions, interference in fuzzy logic, fuzzy if-then rules, Fuzzy implications and Fuzzy algorithms, Fuzzyfications & Defuzzificataions, Fuzzy Controller, Industrial applications | 08 |
| V | Genetic Algorithm(GA): Basic concepts, working principle, procedures of GA, flow chart of GA, Genetic representations, (encoding) Initialization and selection, Genetic operators, Mutation, Generational Cycle, applications. | 08 |
Text Books
- S. Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks,Fuzzy Logic and Genetic Algorithm:Synthesis and Applications” Prentice Hall of India.
- N. P. Padhy,”Artificial Intelligence and Intelligent Systems” Oxford University Press. Reference Books:
- Siman Haykin,”Neural Networks” 3rd Edition Pearson Education
- Timothy J. Ross, “Fuzzy Logic with Engineering Applications” Wiley India.
- Kumar Satish, “Neural Networks” McGraw Hill
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