a seminar report on soft computing - study...
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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.