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A

Seminar report

On

Soft Computing Submitted in partial fulfillment of the requirement for the award of degree

Of MCA

SUBMITTEDTO: SUBMITTED BY:

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Preface

I have made this report file on the topic Soft computing; I have tried my best to elucidate all the

relevant detail to the topic to be included in the report. While in the beginning I have tried to give

a general view about this topic.

My efforts and wholehearted co-corporation of each and everyone has ended on a successful

note. I express my sincere gratitude to …………..who assisting me throughout the preparation of

this topic. I thank him for providing me the reinforcement, confidence and most importantly the

track for the topic whenever I needed it.

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Content

Introduction

What is Soft Computing?

Soft Computing Tools

Importance

Future of Soft Computing

Hard Computing Vs Soft Computing

Conclusion

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ABSTRACT

Soft computing is a term applied to a field within computer science which is characterized by the

use of inexact solutions to computationally-hard tasks such as the solution of NP-complete

problems, for which an exact solution cannot be derived in polynomial time.

Soft Computing became a formal Computer Science area of study in the early 1990's. Earlier

computational approaches could model and precisely analyze only relatively simple systems.

More complex systems arising in biology, medicine, the humanities, management sciences, and

similar fields often remained intractable to conventional mathematical and analytical methods.

That said, it should be pointed out that simplicity and complexity of systems are relative, and

many conventional mathematical models have been both challenging and very productive. Soft

computing deals with imprecision, uncertainty, partial truth, and approximation to achieve

tractability, robustness and low solution cost.

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INTRODUCTION

Soft Computing became a formal Computer Science area of study in the early 1990's.Earlier

computational approaches could model and precisely analyze only relatively simple systems.

More complex systems arising in biology, medicine, the humanities,management sciences, and

similar fields often remained intractable to conventional mathematical and analytical methods.

That said, it should be pointed out that simplicity and complexity of systems are relative, and

many conventional mathematical models have been both challenging and very productive. Soft

computing deals with imprecision, uncertainty,partial truth, and approximation to achieve

tractability, robustness and low solution cost.

The idea of soft computing was initiated in 1981 by Lotfi. A. Zadeh. Generally speaking, soft

computing techniques resemble biological processes more closely than traditional techniques,

which are largely based on formal logical systems, such as sentential logic and predicate logic, or

rely heavily on computer-aided numerical analysis (as in finite element analysis). Soft computing

techniques are intended to complement each other.

Unlike hard computing schemes, which strive for exactness and full truth, soft computing

techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a

particular problem. Another common contrast comes from the observation that inductive

reasoning plays a larger role in soft computing than in hard computing.

Components of soft computing include: Neural Network, Perceptron, Fuzzy Systems,Baysian

Network, Swarm Intelligence and Evolutionary Computation.The highly parallel processing and

layered neuronal morphology with learning abilities of the human cognitive faculty ~the brain~

provides us with a new tool for designing a cognitive machine that can learn and recognize

complicated patterns like human faces and Japanese characters.

The theory of fuzzy logic, the basis for soft computing, provides mathematical power for the

emulation of the higher-order cognitive functions ~the thought and perception processes. A

marriage between these evolving disciplines, such as neural computing, genetic algorithms and

fuzzy logic, may provide a new class of computing systems ~neural-fuzzy systems ~ for the

emulation of higher-order cognitive power.

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What is Soft Computing?

“Soft computing is a collection of methodologies that aim to exploit the tolerance for imprecision

and uncertainty to achieve tractability, robustness, and low solution cost.

Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft

computing is likely to play an increasingly important role in many application areas, including

software engineering. The role model for soft computing is the human mind.”

Soft computing is not precisely defined.

It consists of distinct concepts and techniques which aim to overcome the difficulties

encountered in real world problems.

These problems result from the fact that our world seems to be imprecise, uncertain and difficult

to categorize.

Possibly our world is uncertain really (see Quantum Theory, theory of relativity).

But question what is in reality and what is appeared in mind is senseless (R.A.Wilson, “Quantum

Psychology”)

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Techniques of Soft Computing

Neural Network

First of all, when we are talking about a neural network, we should more properly say "artificial

neural network" (ANN), because that is what we mean most of the time in comp.ai.neural-nets.

Biological neural networks are much more complicated than the mathematical models we use for

ANNs. But it is customary to be lazy and drop the "A" or the "artificial".

There is no universally accepted definition of an NN. But perhaps most people in the field would

agree that an NN is a network of many simple processors ("units"), each possibly having a small

amount of local memory. The units are connected by communication channels ("connections")

which usually carry numeric (as opposed to symbolic) data, encoded by any of various means.

The units operate only on their local data and on the inputs they receive via the connections. The

restriction to local operations is often relaxed during training.

Some NNs are models of biological neural networks and some are not, but historically, much of

the inspiration for the field of NNs came from the desire to produce artificial systems capable of

sophisticated, perhaps "intelligent", computations similar to those that the human brain routinely

performs, and thereby possibly to enhance our understanding of the human brain.

Most NNs have some sort of "training" rule whereby the weights of connections are adjusted on

the basis of data. In other words, NNs "learn" from examples, as children learn to distinguish

dogs from cats based on examples of dogs and cats. If trained carefully, NNs may exhibit some

capability for generalization beyond the training data, that is, to produce approximately correct

results for new cases that were not used for training.

NNs normally have great potential for parallelism, since the computations of the components are

largely independent of each other. Some people regard massive parallelism and high connectivity

to be defining characteristics of NNs, but such requirements rule out various simple models, such

as simple linear regression (a minimal feedforward net with only two units plus bias), which are

usefully regarded as special cases of NNs.

Fuzzy Logic

FL is a problem-solving control system methodology that lends itself to implementation in

systems ranging from simple, small, embedded micro-controllers to large, networked, multi-

channel PC or workstationbased data acquisition and control systems. It can be implemented in

hardware, software, or a combination of both. FL provides a simple way to arrive at a definite

conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. FL's

approach to control problems mimics how a person would make decisions, only much faster.

Genetic Algorithms

A genetic or evolutionary algorithm applies the principles of evolution found in nature to the

problem of finding an optimal solution to a Solver problem. In a "genetic algorithm," the roblem

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is encoded in a series of bit strings that are manipulated by the algorithm; in an "evolutionary

algorithm," the decision variables andproblem functions are used directly. Most commercial

Solver products are based on evolutionary algorithms.

An evolutionary algorithm for optimization is different from "classical" optimization methods in

several ways:

● Random Versus Deterministic Operation

● Population Versus Single Best Solution

● Creating New Solutions Through Mutation

● Combining Solutions Through Crossover

● Selecting Solutions Via "Survival of the Fittest"

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Importance of Soft Computing

The complementarity of FL, NC, GC, and PR has an important consequence: in many cases a

problem can be solved most effectively by using FL, NC, GC and PR in combination rather than

exclusively. A striking example of a particularly effective combination is what has come to be

known as "neurofuzzy systems."

Such systems are becoming increasingly visible as consumer products ranging from air

conditioners and washing machines to photocopiers and camcorders. Less visible but perhaps

even more important are neurofuzzy systems in industrial applications. What is particularly

significant is that in both consumer products and industrial systems, the employment of soft

computing techniques leads to systems which have high MIQ (Machine Intelligence Quotient).

In large measure, it is the high MIQ of SC-based systems that accounts for the rapid growth in

the number and variety of applications of soft computing.

The conceptual structure of soft computing suggests that students should be trained not just in

fuzzy logic, neurocomputing, genetic programming, or probabilistic reasoning but in all of the

associated methodologies, though not necessarily to the same degree.

At present, the BISC Group (Berkeley Initiative on Soft Computing) comprises close to 600

students, professors, employees of private and non-private organizations and, more generally,

individuals who have interest or are active in soft computing or related areas. Currently, BISC

has over 50 Institutional Affiliates, with their ranks continuing to grow in number.

At Berkeley, BISC provides a supportive environment for visitors, postdocs and students who

are interested in soft computing and its applications. In the main, support for BISC comes from

member companies.

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FUTURE OF SOFT COMPUTING

• Soft computing is likely to play an especially important role in science and engineering, but

eventually its influence may extend much farther.

• Soft computing represents a significant paradigm shift in the aims of computing. A shift which

reflects the fact that the human mind, unlike present day computers, possesses a remarkable

ability to store and process information which is pervasively imprecise,uncertain and lacking in

categoricity.

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Hard Computing Vs Soft Computing

1) Hard computing, i.e., conventional computing, requires a precisely stated analytical

model and often a lot of computation time. Soft computing differs from conventional (hard)

computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth,

and approximation. In effect, the role model for soft computing is the human mind.

2) Hard computing based on binary logic, crisp systems, numerical analysis and crisp

software but soft computing based on fuzzy logic, neural nets and probabilistic reasoning.

3) Hard computing has the characteristics of precision and categoricity and the soft

computing, approximation and dispositionality. Although in hard computing, imprecision and

uncertainty are undesirable properties, in soft computing the tolerance for imprecision and

uncertainty is exploited to achieve tractability, lower cost, high Machine Intelligence Quotient

(MIQ) and economy of communication

4) Hard computing requires programs to be written; soft computing can evolve its own

programs

5) Hard computing uses two-valued logic; soft computing can use multivalued or fuzzy

logic

6) Hard computing is deterministic; soft computing incorporates stochasticity

7) Hard computing requires exact input data; soft computing can deal with ambiguous and

noisy data

8) Hard computing is strictly sequential; soft computing allows parallel computations

9) Hard computing produces precise answers; soft computing can yield approximate

answers

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Conclusion

The complementarity of FL, NC, GC, and PR has an important consequence: in many cases a

problem can be solved most effectively by using FL, NC, GC and PR in combination rather than

exclusively. A striking example of a particularly effective combination is what has come to be

known as "neuro fuzzy systems."

Such systems are becoming increasingly visible as consumer products ranging from air

conditioners and washing machines to photocopiers and camcorders. Less visible but perhaps

even more important are neuro fuzzy systems in industrial applications.

What is particularly significant is that in both consumer products and industrial systems, the

employment of soft computing techniques leads to systems which have high MIQ (Machine

Intelligence Quotient). In large measure, it is the high MIQ of SC based systems that accounts

for the rapid growth in the number and variety of applications of soft computing.

The successful applications of soft computing suggest that the impact of soft computing will be

felt increasingly in coming years. Soft computing is likely to play an especially important role in

science and engineering, but eventually its influence may extend much farther.

In many ways, soft computing represents a significant paradigm shift in the aims of computing -

a shift which reflects the fact that the human mind, unlike present day computers, possesses a

remarkable ability to store and process information which is pervasively imprecise, uncertain and

lacking in category city.