Application
of Soft Computing:
There is a
major paradigm shift in the methods of computing which reflects the remarkable
growth of the human mind. One major change that we’ve witnessed is the
evolution of hard computing to soft computing.
The
techniques used by soft computing have basically originated from various
mathematical concepts, like fuzzy logic.
Soft
computing provides insights into real-world problems and is just not limited to
theory. It has tolerance for uncertainty and is imprecise in nature due to
which it has spread widely into various industries.
So what is
soft computing?
Soft
computing is the process of solving real-life complex problems using
approximate calculations and gives solutions that are not very specific in
nature just like the human brain works, which, unlike traditional computing,
focuses on impartial truths and approximates.
The
constituents of soft computing are:
- Fuzzy
Logic (FL)
- Evolutionary
Computation (EC)
- Machine
Learning (ML) and
- Probabilistic
Reasoning (PR)
There are
various problems in the world that require vast resources and computations to
be made which cannot be solved, just by logical means. Soft computing has
emerged as a way of solving problems, the way humans do.
Let’s take a
look at some of the applications of soft computing across different industries-
Handwritten Script Recognition using Soft
Computing
Handwritten
Script Recognition is one of the demanding parts of computer science. It can
translate multilingual documents and sort the various scripts accordingly.
It uses the
concept of “block-level technique” where the system recognizes the particular
script from a number of script documents given.
It uses a
Discrete Cosine Transform (DCT), and discrete wavelets Transform (DWT)
together, which classify the scripts according to their features.
Image Processing and Data
Compression using Soft Computing
Image
analysis is one of the most important parts of the medical field. It is a
high-level processing technique which includes recognition and bifurcation of
patterns.
Using soft
computing solves the problem of computational complexity and efficiency in the
classification. Techniques of soft computing include Genetic Algorithms,
Genetic Programming, Classifier Systems, Evolution Strategies, artificial life,
and a few others, which are used here.
These
algorithms give the fastest solutions to pattern recognition. These help in
analyzing the medical images obtained from microscopes as well as examine the
X-rays.
Use of Soft Computing in Automotive Systems and Manufacturing
The use of
soft computing has solved a major misconception that the automobile industry is
slow to adapt. Fuzzy logic is a technique used in vehicles to build classic
control methods.
It takes the
example of human behavior, which is described in the forms of rule – “If-Then
“statements.
The logic
controller then converts the sensor inputs into fuzzy variables that are then
defined according to these rules. Fuzzy logic techniques are used in engine control,
automatic transmissions, antiskid steering, etc.
Soft Computing based Architecture
An
intelligent building takes inputs from the sensors and controls effectors by
using them.
The
construction industry uses the technique of DAI (Distributed Artificial
Intelligence) and fuzzy genetic agents to provide the building with
capabilities that match human intelligence.
The fuzzy
logic is used to create behavior-based architecture in intelligent buildings to
deal with the unpredictable nature of the environment, and these agents embed
sensory information in the buildings.
Soft Computing and Decision Support System
Soft
computing gives an advantage of reducing the cost of the decision support
system. The techniques are used to design, maintain, and maximize the value of
the decision process.
The first
application of fuzzy logic is to create a decision system that can predict any
sort of risk. The second application is using fuzzy information that selects
the areas which need replacement.
Soft Computing Techniques in Power System Analysis
Soft
computing uses the method of Artificial Neural Network (ANN) to predict any
instability in the voltage of the power system.
Using the
ANN, the pending voltage instability can be predicted. The methods which are
deployed here, are very low in cost.
Soft Computing Techniques in Bioinformatics
The
techniques of soft computing help in modifying any uncertainty and indifference
that biometrics data may have.
Soft
computing is a technique that provides distinct low-cost solutions with the
help of algorithms, databases, Fuzzy Sets (FSs), and Artificial Neural Networks
(ANNs).
These
techniques are best suited to give quality results in an efficient way.
Soft Computing in Investment and Trading
The data
present in the finance field is in opulence and traditional computing is not
able to handle and process that kind of data.
There are
various approaches done through soft computing techniques that help to handle
noisy data.
Pattern
recognition technique is used to analyze the pattern or behavior of the data
and time series is used to predict future trading points.
Recent
developments in soft computing
People have
started using techniques of soft computing like fuzzy sets theory, neural nets,
fuzzy neuro system, adaptive neuro-fuzzy inference system (ANFIS), for driving
various numerical simulation analysis.
Soft
computing has helped in modeling the processes of machines with the help of
artificial intelligence. Also, there are certain areas where soft computing is
in budding stages only and is expected to see a massive evolution:
- Big
Data
- Recommender
system
- Behavior
and decision science
- Mechanical
Engineering
- Computer
Engineering
- Civil
Engineering
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