Sc paper sol nov-dec 17.docx - Marathwada Shikshan Prasarak Mandal\u2019s Deogiri Institute of Engineering and Management Studies Aurangabad Department of

# Sc paper sol nov-dec 17.docx - Marathwada Shikshan Prasarak...

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Marathwada Shikshan Prasarak Mandal’s Deogiri Institute of Engineering and Management Studies, Aurangabad Department of Computer Science and Engineering Paper Solution SC Nov/Dec 2017 Class : BE CSE _ __________________________________________________________________________ Q. 1 Answer the following. 1) Define Soft Computing? Differentiate between soft computing and hard computing. Ans: It is the composition of methodologies designed to model and enable solution to real world problems. Soft Computing aims to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve close resemblance with human decisions. Soft Computing vs Hard Computing:- 1. Soft Computing is tolerant of imprecision, uncertainty, partial truth and approximation whereas Hard Computing requires a precisely state analytic model. 2. Soft Computing is based on fuzzy logic , neural sets, and probabilistic reasoning whereas Hard Computing is based on binary logic, crisp system, numerical analysis and crisp software. 3. Soft computing has the characteristics of approximation and dispositionality whereas Hard computing has the characteristics of precision and categoricity. 4. Soft computing can evolve its own programs whereas Hard computing requires programs to be written. 5. Soft computing can use multivalued or fuzzy logic whereas Hard computing uses two-valued logic. 6. Soft computing incorporates stochasticity whereas Hard computing is deterministic. 7. Soft computing can deal with ambiguous and noisy data whereas Hard computing requires exact input data. 8. Soft computing allows parallel computations whereas Hard computing is strictly sequential. 9. Soft computing can yield approximate answers whereas Hard computing produces precise answers. 2) What is linearly seperable and linearly non-seperable problems? Explain it with example. Ans:Consider two-input patterns being classified into two classes as shown in figure 2.9 . Each point with either symbol of or represents a pattern with a set of values . Each pattern is classified into one of two classes. Notice that these classes can be separated with a single line . They are known as linearly separable patterns. Linear separability refers to the fact that classes of patterns with - dimensional vector can be separated with a single decision surface . In the case above, the line represents the decision surface.
The processing unit of a single-layer perceptron network is able to categorize a set of patterns into two classes as the linear threshold function defines their linear separability. Conversely, the two classes must be linearly separable in order for the perceptron network to function correctly [ Hay99 ]. Indeed, this is the main limitation of a single-layer perceptron network. The most classic example of linearly inseparable pattern is a logical exclusive-OR (XOR) function. Shown in figure 2.10 is the illustration of XOR function that two classes, 0 for black dot and 1 for white dot, cannot be separated with a single line. The solution seems that patterns of can be logically classified with two lines and [ BJ91 ].

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• Spring '16
• 213
• Machine Learning, Artificial neural network, neural network, Self-organizing map

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