JNTUK B.Tech Neural Networks & Soft Computing (Elective – IV) gives you detail information of Neural Networks & Soft Computing (Elective – IV) R13 syllabus It will be help full to understand you complete curriculum of the year.
Course Objectives
- To have a detailed study of neural networks, Fuzzy Logic and uses of Heuristics based on human experience.
- To Familiarize with Soft computing concepts.
- To introduce the concepts of genetic algorithm and its applications to soft computing using some applications
Course Outcomes
- Identify and describe soft computing techniques and their roles in building intelligent machines.
- Recognize the feasibility of applying a soft computing methodology for a particular problem.
- Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems.
Syllabus
UNIT I: INTRODUCTION: what is a neural network? Human Brain, Models of a Neuron, Neural networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural
UNIT II: LEARNING PROCESS: Error Correction learning, Memory based learning, Hebbian learning, Competitive, Boltzmann learning, Credit Assignment Problem, Memory, Adaption, Statistical nature of the learning process.
UNIT III: CLASSICAL & FUZZY SETS: Introduction to classical sets – properties, operations and relations; Fuzzy sets – memberships, uncertainty, operations, properties, fuzzy relations, cardinalities, membership functions.
UNIT IV: FUZZY LOGIC SYSTEM COMPONENTS: Fuzzification, Membership value assignment, development of rule base and decision making system, Defuzzification to crisp sets, Defuzzification methods
UNIT V: CONCEPT LEARNING: Introduction, A concept learning task, Concept learning as search, Find-S: finding a maximally specific hypothesis, Version spaces and the candidate elimination algorithm DECISION TREE LEARNING: Introduction, Decision tree representation, Appropriate problems for decision tree learning, The basic decision tree learning algorithm, Hypothesis space search in decision tree learning
UNIT VI: GENETIC ALGORITHMS: Motivation, Genetic Algorithms, an Illustrative Example, Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms
TEXT BOOKS
- Neural networks A comprehensive foundations, Simon Hhaykin, Pearson Education 2nd edition2004
- Neural Networks, Fuzzy Logic, Genetic Algorithms: Sysnthesis and Applications by Rajasekharan and Pai, PHI Publications
- Machine Learning, Tom M. Mitchell, MGH
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