Soft Computing Techniques detailed syllabus for Geoinformatics Engineering (Geo) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Geo students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Geoinformatics Engineering 5th Sem scheme and its subjects, do visit Geo 5th Sem 2021 regulation scheme. For Professional Elective-III scheme and its subjects refer to Geo Professional Elective-III syllabus scheme. The detailed syllabus of soft computing techniques is as follows.
Course Objectives:
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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
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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
Course Outcomes:
- On completion of the course, the student is expected to
- Understand the necessity of soft computing techniques and fundamentals of Artificial Neural Networks
- Imparts the concepts of uncertainty and its impacts on artificial intelligence
- Helps to realize the merits of hybrid computing techniques
- Introduces the concepts of heuristic search methods and optimization of solutions
- Gain knowledge on utility of soft computing on multidisciplinary problems
Text Books:
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Reference Books:
- Introduction to Artificial Neural Systems by Jacek.M Zurada, Jaico Publishing House,1992.
- Timothy J.Ross: Fuzzy Logic Engineering Applications, 4th Edition, 2016, McGraw Hill,NewYork,1997.
- Laurene Fauseett: Fundamentals of Neural Networks, Pearson 2004, Prentice Hall India, New Delhi,1994.
- George J.Klir and Bo Yuan, Fuzzy Sets and Fuzzy Logic, Prentice Hall Inc., New Jersey,1995
- Nih.J. Ndssen Artificial Intelligence, Harcourt
For detailed syllabus of all the other subjects of Geoinformatics Engineering 5th Sem, visit Geo 5th Sem subject syllabuses for 2021 regulation.
For all Geoinformatics Engineering results, visit Anna University Geo all semester results direct link.