Soft Computing detailed syllabus scheme for Information Technology (IT), 2019 regulation has been taken from the MU official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.
For 7th Sem Scheme of Information Technology (IT), 2019 Pattern, do visit IT 7th Sem Scheme, 2019 Pattern. For the Department Level Optional Course-3 scheme of 7th Sem 2019 regulation, refer to IT 7th Sem Department Level Optional Course-3 Scheme 2019 Pattern. The detail syllabus for soft computing is as follows.
Soft Computing Syllabus for Information Technology BE 7th Sem 2019 Pattern Mumbai University
Course Objectives:
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Course Outcomes:
Students will be able to:
- List the facts and outline the different process carried out in fuzzy logic, ANN and Genetic Algorithms.
- Explain the concepts and meta-cognitive of soft computing.
- Apply Soft computing techniques the solve character recognition, pattern classification, regression and similar problems.
- Outline facts to identify process/procedures to handle real world problems using soft computing.
- Evaluate various techniques of soft computing to defend the best working solutions.
- Design hybrid system to revise the principles of soft computing in various applications.
Prerequisites:
C++/Java/ Matlab programming.
Module I
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Module II
Fuzzy Rules, Reasoning, and Inference System Fuzzy Rules: Fuzzy If-Then Rules, Fuzzy Reasoning Fuzzy Inference System ( FIS): Mamdani FIS, Sugeno FIS, Comparison between , Mamdani and Sugeno FIS. 06 CO1 CO2
Module III
Neural Network-I Introduction: What is a Neural network? Fundamental Concepts, Basic Models of Artificial Neural Networks, Arificial Intelligence and Neural Networks, McCulloch-Pitts Neuron Learning: Error-Correction Learning, Memory based Learning, Hebbian learning, Competitive Learning, Boltzmann Learning Perceprton: Perceprton Learning Rule, Perceptron Learning Algorithm, Perceprton Convergence Theorem, Perceptron learning and Non-separable sets. 09 CO1 CO2
Module IV
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Module V
Genetic Algorithm An Introduction to genetic Algorithms: What Are Genetic Algorithms? Robustness of Traditional Optimization and Search Methods, The Goals of Optimization, How Are Genetic Algorithms Different from Traditional Methods?, A Simple Genetic Algorithm Genetic Algorithms at Worka Simulation by hand, Grist for the Search MillImportant Similarities, Similarity Templates (Schemata), Learning the Lingo. Genetic Algorithms: Mathematical Foundations Who Shall Live and Who Shall Die? The Fundamental Theorem, Schema Processing at Work: An Example by Hand Revisited, The Two-armed and n-armed Bandit Problem, How Many Schemata Are Processed Usefully? The Building Block Hypothesis, Another Perspective: The Minimal Deceptive Problem, Schemata Revisited: Similarity Templates as Hyperplanes, Implementation of a Genetic Algorithm: Data Structures, Reproduction, Crossover, and Mutation, A Time to Reproduce, a Time to Cross, Get with the Main Program, How Well Does it Work? Mapping Objective Functions to Fitness Form, Fitness Scaling, Codings, A Multiparameter, Mapped, Fixed-Point Coding, Discretization, Constraints. Algorithm for Handwriting Recognition Using GA Generation of Graph, Fitness Function of GA: Deviation between Two Edges, Deviation of a Graph, Crossover: Matching of Points, Generate Adjacency Matrix, Find Paths, Removing and Adding Edges, Generation of Graph Results of Handwriting Recognition: Effect of Genetic Algorithms, Distance Optimization, Style Optimization 10 CO1 CO3 CO6
Module VI
Hybrid Computing Introduction, Neuro-Fuzzy Hybrid Systems, Adaptive Neuro-Fuzzy Inference System (ANIFS): Introduction, ANFS Architecture, Hybrid Learning Algorithm, ANFIS as a Universal Approximator, Simulation Examples: Two-input Sinc Function and Three Input Nonlinear Function Genetic Neuro-Hybrid Systems: Properties of Genetic Neuro-Hybrid Systems, genetic Algorithm based Back-propagation Network, Advantages of Neuro-Genetic Hybrids, Genetic Fuzzy Hybrid and Fuzzy Genetic Hybrid Systems Genetic Fuzzy Rule based Systems, Advantages of Genetic Fuzzy Hybrids 09 CO4 CO6
Text Books:
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Reference Books:
- Anupam Shukla, Ritu Tiwari, Rahul Kala, Real Life Applications of Soft Computing, CRC Press, Taylor & Francis Group, 2010.
- Genetic Algorithms and Genetic Programming Modern Concepts and Practical Applications 2009 Michael Affenzeller, Stephan Winkler, Stefan Wagner, and Andreas Beham, CRC Press
- Laurene V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms And Applications, Pearson
Assessment:
Internal Assessment for 20 marks: Consisting of Two Compulsory Class Tests Approximately 40% to 50% of syllabus content must be covered in First test and remaining 40% to 50% of syllabus contents must be covered in second test. End Semester Theory Examination: Some guidelines for setting the question papers are as:
- Weightage of each module in end semester examination is expected to be/will be proportional to number of respective lecture hours mentioned in the syllabus.
- Question paper will comprise of total six questions, each carrying 20 marks.
- Q.1 will be compulsory and should cover maximum contents of the syllabus.
- Remaining question will be mixed in nature (for example if Q.2 has part
- from module 3 then part
- will be from any other module. (Randomly selected from all the modules)
- Total four questions need to be solved.
For detail Syllabus of all subjects of Information Technology (IT) 7th Sem 2019 regulation, visit IT 7th Sem Subjects of 2019 Pattern.