RGPV CS VIII SEMESTER SOFT COMPUTING (CS-801)SYLLABUS

TAGS:-RGPV CS 8TH SEM SYLLABUS I RGPV CS 8TH SEM SOFT COMPUTING SYLLABUS I RGPV CS 801 SYLLABUS I SOFT COMPUTING SYLLABUS I RGTU CS 8TH SEM SYLLABUS I RGPV CS 8-1 SOFT COMPUTING SYLLABUS I RGPV SYLLABUS FOR SOFT COMPUTING

RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA, BHOPAL
PROGRAMME :B.E. Computer Science & Engineering, VIII Semester
Course: CS-801 SOFT COMPUTING

RGPV CS VIII SEMESTER SOFT COMPUTING (CS-801)SYLLABUS

Unit – I

Soft Computing:Introduction of soft computing, soft computing vs. hard computing,various types of soft computing techniques,applications of soft computing.Artificial Intelligence: Introduction,Various types of production systems, characteristics of production systems,breadth first search, depth first search techniques, other Search Techniques like hill Climbing, Best first Search, A* algorithm, AO* Algorithms and various types of control strategies. Knowledge representation issues, Prepositional and predicate logic, monotonic and non monotonic reasoning,forward Reasoning, backward reasoning, Weak & Strong Slot & filler structures, NLP.

Unit – II

Neural Network:Structure and Function of a single neuron:Biological neuron,artificial neuron, definition of ANN,Taxonomy of neural net,Difference b/w ANN and human brain,characteristic and applications of ANN, single layer network, Perceptron training algorithm,Linear separability, Widrow & Hebb;s learning rule/Delta rule, ADALINE, MADALINE, AI v/s ANN.Introduction of MLP,different activation functions,Error back propagation algorithm,derivation of BBPA, momentum, limitation, characteristics and application of EBPA,

Unit – III

Counter propagation network:architecture,functioning & characteristics of counter Propagation network,Hop field/ Recurrent network,configuration, stability constraints, associative memory, and characteristics,limitations and applications.Hopfield v/s Boltzman machine.Adaptive Resonance Theory:Architecture,classifications,Implementation and training.Associative Memory.

Unit – IV

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

Unit – V 

Genetic algorithm:Fundamental,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 methods.

Reference Books :

• S, Rajasekaran & G.A. Vijayalakshmi Pai, Neural Networks, Fuzzy Logic & Genetic

• Algorithms, Synthesis & applications, PHI Publication.

• S.N. Sivanandam & S.N. Deepa, Principles of Soft Computing, Wiley Publications

• Rich E and Knight K, Artificial Intelligence, TMH, New Delhi.

• Bose, Neural Network fundamental with Graph , Algo.& Appl, TMH

• Kosko: Neural Network & Fuzzy System, PHI Publication

• Klir & Yuan ,Fuzzy sets & Fuzzy Logic: Theory & Appli.,PHI Pub.

• Hagen, Neural Network Design, Cengage Learning