Syllabus of Soft Computing

RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA, BHOPAL

PROGRAMME: B.E.

Computer Science & Engineering, VIII semester

Soft Computing 

CS801

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 between ANN and human brain,

characteristics 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, Hopfield/ 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 : 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