GI3015 Soft Computing Techniques Syllabus:

GI3015 Soft Computing Techniques Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 The objective of the course is to make the students to understand the concepts of Artificial Neural Network, Fuzzy logic and Genetic algorithms and also their application in Geomatics.

UNIT I SOFT COMPUTING AND ARTIFICIAL NEURAL NETWORKS

Soft Computing: Introduction – soft computing vs. hard computing – soft computing techniques – applications of soft computing – ANN: Structure and Function of a single neuron: Biological neuron, artificial neuron, definition of ANN, Taxonomy of neural net, Difference between ANN and human brain, characteristics and applications of ANN, single layer network, Perceptron training algorithm, Linear separability, Widrow & Hebbian learning rule/Delta rule, ADALINE, MADALINE and BPN.

UNIT II FUZZY SYSTEMS

Fuzzy Logic: Fuzzy set theory, Fuzzy set versus crisp set, Crisp and fuzzy relations – introduction and features of membership functions, Fuzzy rule base system: fuzzy propositions, formation, decomposition & aggregation of fuzzy rules, fuzzy reasoning, fuzzy inference systems, fuzzy decision making.

UNIT III NEURO-FUZZY MODELLING

Adaptive Neuro-Fuzzy Inference Systems – Architecture – Hybrid Learning Algorithm – Learning Methods that Cross-fertilize ANFIS and RBFN – Coactive Neuro Fuzzy Modeling – Framework Neuron Functions for Adaptive Networks – Neuro Fuzzy Spectrum

UNIT IV GENETIC ALGORITHM

Genetic algorithm : Fundamentals, basic concepts, working principle, encoding, fitness function, reproduction, Genetic modeling: Inheritance operator, cross over, inversion & deletion, mutation operator, Bitwise operator, Generational Cycle, Convergence of GA, Applications & advances in GA, Differences & similarities between GA & other traditional method

UNIT V APPLICATIONS OF SOFT COMPUTING IN GEOMATICS

Image registration – Object recognition – Automated feature extraction – navigation – Integration of soft computing and GIS for flood forecasting and monitoring, Landslide susceptibility, Highway alignment, smart city planning, agriculture, solid waste disposal

TOTAL:45 PERIODS
COURSE OUTCOMES:

On completion of the course, the student is expected to
CO1 Understand the necessity of soft computing techniques and fundamentals of Artificial Neural Networks
CO2 Imparts the concepts of uncertainty and its impacts on artificial intelligence
CO3 Helps to realize the merits of hybrid computing techniques
CO4 Introduces the concepts of heuristic search methods and optimization of solutions
CO5 Gain knowledge on utility of soft computing on multidisciplinary problems

TEXTBOOKS:

1. Freeman J.A. and Skapura B.M., “Neural Networks, Algorithms Applications and Programming Techniques”, Pearson, 2002.
2. Jang J.S.R.,Sun C.T and Mizutami E – Neuro Fuzzy and Soft computing ,Prentice hall New Jersey, Pearson, 2015.

REFERENCES

1. Introduction to Artificial Neural Systems by Jacek.M Zurada, Jaico Publishing House,1992.
2. Timothy J.Ross: Fuzzy Logic Engineering Applications, 4th Edition, 2016, McGraw Hill,NewYork,1997.
3. Laurene Fauseett: Fundamentals of Neural Networks, Pearson 2004, Prentice Hall India, New Delhi,1994.
4. George J.Klir and Bo Yuan, Fuzzy Sets and Fuzzy Logic, Prentice Hall Inc., New Jersey,1995
5. Nih.J. Ndssen Artificial Intelligence, Harcourt