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Cs 801 Practicals

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PRACTICAL FILE ONSoft Computing (CS-801)

Department of CSE & IT

Submitted To:Mr. Rakesh Singh Dept of CS

Submitted By:Monika Sharma 0916CS051039




Department of CSE & IT Lab Manual (In accordance with RGTU syllabus)



Soft Computing Lab



CS 801


: :

8th Sem CSE & IT




Lab Manual of Soft Computing (CS 801)

Suggestions from Principal:

Enhancement if any:



INDEXS. No.1 2 3 4 5 6

CONTENTIntroduction About Lab Syllabus Guidelines to Students List of Experiments Solutions for Programs Practical Quiz

Page. No

Signature of the faculty

INTRODUCTION ABOUT LABThere are 60 systems installed in this Lab. Their configurations are as follows: Processor RAM Hard Disk Monitor Mouse Key Board Network Topology Network Interface card : : : : : : : : Present Dual Core 1.7 GHz 512MB 80 GB 15 LCD Color Monitor Optical Mouse 105 MMX Key Board Star Topology

Software 1. All systems are configured in Windows XP as per their lab requirement. 2. Each student has a separate login for database access Oracle 9i client version is installed in all systems. On the server, account for each student has been created. This is very useful because students can save their work (scenarios, pl/sql programs, data related projects, etc) in their own accounts. Each student work is safe and secure from other students. 1. Latest Technologies like DOT NET and J2EE are installed in some systems. Before submitting their final project, they can start doing mini project from 2nd year onwards. 2. Softwares installed: C, C++, JDK1.5, MASM, OFFICE-XP, J2EE and DOT NET, Rational Rose.

CS801 Soft Computing

Branch: B.E. Computer Science & Engineering, VIII semester Course: CS801 Soft Computing

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 & Hebbs 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 methods.

IT802 Soft Computing

Branch: B.E. Information Technology, VIII Semester

Course: IT802 Soft Computing

UNITI Introduction to Neural Network: Concept, biological neural network, evolution of artificial neural network, McCulloch-Pitts neuron models, Learning (Supervise & Unsupervise) and activation function, Models of ANN-Feed forward network and feed back network, Learning Rules- Hebbian, Delta, Perceptron Learning and Windrow-Hoff, winner take all. UNITII Supervised Learning: Perceptron learning,- Single layer/multilayer, linear Separability, Adaline,Madaline, Back propagation network, RBFN. Application of Neural network in forecasting, data compression and image compression. UNITIII

Unsupervised learning: Kohonen SOM (Theory, Architecture, Flow Chart, Training Algorithm) Counter Propagation (Theory , Full Counter Propagation NET and Forward only counter propagation net),ART (Theory, ART1, ART2). Application of Neural networks in pattern and face recognition, intrusion detection, robotic vision. UNIT-IV Fuzzy Set: Basic Definition and Terminology, Set-theoretic Operations, Member Function,Formulation and Parameterization, Fuzzy rules and fuzzy Reasoning, Extension Principal and Fuzzy Relations, Fuzzy if-then Rules, Fuzzy Inference Systems. Hybrid system including neuro fuzzy hybrid, neuro genetic hybrid and fuzzy genetic hybrid, fuzzy logic controlled GA. Application of Fuzzy logic in solving engineering problems. UNIT-V Genetic Algorithm: Introduction to GA, Simple Genetic Algorithm, terminology and operators of GA (individual, gene, fitness, population, data structure, encoding, selection, crossover, mutation, convergence criteria). Reasons for working of GA and Schema theorem, GA optimization problems including JSPP (Job shop scheduling problem), TSP (Travelling salesman problem), Network design routing, timetabling problem. GA implementation using MATLAB.

Guidelines to Students

1. Equipment in the lab for the use of student community. Students need to maintain a proper decorum in the computer lab. Students must use the equipment with care. Any damage is caused is punishable. 2. Students are required to carry their observation / programs book with completed exercises while entering the lab. 3. Students are supposed to occupy the machines allotted to them and are not supposed to talk or make noise in the lab. The allocation is put up on the lab notice board. 4. Lab can be used in free time / lunch hours by the students who need to use the systems should take prior permission from the lab in-charge.

5. Lab records need to be submitted on or before date of submission. 6. Students are not supposed to use usb device

List of ExperimentsAim1 2 3

ExperimentsStudy of Biological Neural Network Study of Artificial Neural Network Write a program of Perceptron Training Algorithm. Write a program to implement Hebbs rule Write a program to implement of delta rule Write a program for Back propagation Algorithm Write a program for Back Propagation Algorithm by second method Write a program to implement logic gates Study of genetic algorithm Study of Genetic programming (Content Beyond the Syllabus)






7 8 9 10

Experiment No. 1Aim: Study of BNN:Neural networks are inspired by our brains. A biological neural network describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signaling targets define a recognizable circuit. Communication between neurons often involves an electrochemical process. The interface through which they interact with surrounding neurons usually consists of several dendrites (input connections), which are connected via synapses to other neurons, and one axon (output connection). If the sum of the input signals surpasses a certain threshold, the neuron sends an action potential (AP) at the axon hillock and transmits this electrical signal along the axon. The control unit - or brain - can be divided in different anatomic and functional sub-units, each having certain tasks like vision, hearing, motor and sensor control. The brain is connected by nerves to the sensors and actors in the rest of the body. The brain consists of a very large number of neurons, about 1011 in average. These can be seen as the basic building bricks for the central nervous system (CNS). The neurons are interconnected at points called synapses. The complexity of the brain is due to the massive number of highly interconnected simple units working in parallel, with an individual neuron receiving input from up to 10000 others. The neuron contains all structures of an animal cell. The complexity of the structure and of the processes in a simple cell is enormous. Even the most sophisticated neuron models in artificial neural networks seem comparatively toy-like. Structurally the neuron can be divided in three major parts: the cell body (soma), the dendrites, and the axon. The cell body contains the organelles of the neuron and also the `dendrites' are originating there. These are thin and widely branching fibers, reaching out in different directions to make connections to a larger number of cells within the cluster.

Input connections are made from the axons of o