What is Soft Computing and Its Applications and Techniques?

What is Soft Computing and Its Applications and Techniques?

Soft Computing | Tutorial | Journal

 INTRODUCTION – What is Soft Computing is an emerging approach to computing that gives the remarkable ability of the human mind to argue and learn in the atmosphere of uncertainty and distrust.

Soft Computing is based on some biological induced methods such as genetics, development, and behavior, the warm of particles, the human nervous system, etc. The journal of Soft Computing has more demand in the International market because this computing work on the real time application areas such as Fuzzy Logic, Expert System, Computational computing, and Artificial Neural network System.

soft computing

Now Soft Computing is the only solution when we do not have any mathematical modeling of problem-solving (i.e., algorithm), in real-time, there is a need to solve a complex problem, adapt with the changed scenario and be implemented with parallel computing. It has massive applications in many application zones such as medical diagnosis, computer vision, machine intelligence, weather forecasting, network optimization, LSI design, pattern recognition, handwritten character improvement etc.

Importance of Soft Computing

The supplementation of FL, NC, GC, and PR is an important result: In many cases, any problem can be solved most effectively by using FL, NC, GC and PR rather than specially in combination. A great example of a particularly effective combination is known as “Neurofjie System”. Such systems are increasingly seen as a consumer product ranging from air conditioners and washing machines to photocopiers and camcorders. There are less visible but perhaps even more important Neurofjie systems in industrial applications. It is especially important that in both consumer products and industrial systems, the use of soft computing technologies leads to systems that have high MIQ (Machine Intelligence Quota). 

Application Soft computing

There are various Applications of Soft Computing are:

  • Consumer appliance like AC, Refrigerator, Heaters, Washing machine.Robotic works in the form of Emotional Pet robots.
  • Food preparation devices are Microwave and Rice cookers.
  • For amusing gaming playing product like Checker and Poker etc.
  • Recognition for Handwriting.
  • Data compression/Image Processing
  • For Architecture
  • System to Decision-support
  • Polymer Processing
  • Architectures Engineering
  • Applied Mathematics Engineering
  • Intelligent Instrumentation
  • Turning for Cutting Tools

Some stunning Application areas of Soft Computing are:

  • Actuarial Science
  • Agricultural Production Engineering
  • Medicine and Biology Application
  • Construction and Design Engineering
  • Computer Engineering
  • Sin Forecasting
  • Computational Process
  • Natural Environmental Engineering
  • Fault-Tolerance
  • Machine Learning
  • Signal processing
  • Mechanical engineering
  • Materials Engineering
  • Disease diagnosis
  • Nano Technology
  • Pattern Recognition

Advantages of Soft Computing

These are many Advantages of Soft Computing  are:

  • Work as human being reasoning
  • Nearest human thinking
  • Biological inspiration
  • Tolerance to imprecision
  • Can be captured uncertainty and vagueness values
  • Perceive Linguistic Variables
  • Work in equations and conditions

Such as  <if animal = cow>

Then take milk

Principles of Soft Computing

Principles Of Soft Computing accepts many topics such as Defuzzification, Special Networks, Membership Functions, and Supervised Learning Network.


Diffusion is the systemically model of creating a quantitative output in fuzzy logic, looking at fuzzy logic, fuzzy sets and related membership degrees. Mostly it required in the fuzzy control system. In order to change the fuzzy set description as a result of an action in a specific value for a control variable, some process of diffusion is required.

Many methods are:

  • Center of area.
  • Center of gravity
  • Extended center of area.
  • Extended quality method.
  • Fuzzy clustering defuzzification.
  • Fuzzy mean.
  • First of maximum.

Supervised Learning Network:

Supervised Learning concept related to artificial intelligence and Machine learning, in which you have provided both value like as input and output. In which when we set the new input in M = f(N) equation (N is input and M is output).

If output value is exist in category, then classification trouble will be yield such as “One” or “Two”.

If real data is presented in output variable, then Regression problems will be produced.

Special Networks:

  • Boltzmann network
  • Cascade correlation net
  • Probabilistic neural net
  • Cauchy and Gaussian net
  • Cognitron and neocognitron nets
  • Spatia-temporal network
  • Optical neural net
  • Simulated annealing network
  • Cellular neural network
  • Logicon neural network

Membership Functions:

If we define Membership function then it is fuzziness in fuzzy set different element in the set, these can be discrete or continuous. Membership functions are represented as pictorial form.

Soft Computing vs Hard Computing

  • Soft Computing is advance to Hard Computing.
  • Soft Computing work on Fuzzy logic and Genetic Algorithms.
  • Soft Computing not required the any program, it includes in own program.
  • Soft computing performs in  Parallel Computation.
  • Soft Computing produces the approximate output.
  • Hard Computing depends on Conventional Computing.
  • Hard Computing works on the binary system and cris software.
  • Hard Computing need a well written programs.
  • Hard Computing work in Sequential Computation.
  • Hard Computing result produces in precise values.

Applied Soft Computing

Applied Soft Computing is an part of international magazine that promotes the integrated approach of soft computing to fix current life troubles. A compilation of Soft Computing functionality aimed at exploiting impurity, uncertainty and tolerance for partial truths to achieve tractability, robustness and reduced solution costs. The primary aim of the Applied Soft Computing is to publish the excellent quality research in different applications areas of the Fuzzy Logic, Neural Networks, Evolutionary Computing, and Genetic algorithms.

Soft Computing Techniques

Artificial Neural Networks:

Neural Networks in Soft Computing  – Human brains in a way describe the real world conditions, which computers cannot. In order to solve this issue, for the first time, neural networks were developed in the 1950s. An artificial neural networkis an attempt to emulate a network of neurons that make a human brain so that computers can be able to learn things and make decisions in a human way. ANN is made by regular computer programming as if they are mutually associated with brain cells.

Fuzzy logic System:

Fuzzy Logic in Soft Computing – Fuzzy logic is a mathematical logic, which attempts to solve problems with an open, imprecise spectrum of data that makes it possible to get an array of precise findings. Fuzzy logic is designed to be considered the best possible decision by considering all available information and looking an input.

 Genetic Algorithm:

Genetic Algorithm in Soft Computing  – Nature is and will always be an amazing source of inspiration for all of mankind. Genetic algorithms(GA) take all their inspiration from nature, and there are no less genetic algorithms based on search-based algorithms that find its roots in natural selection and concepts of genetics. The genetic algorithm is also a subset of a large branch of computation (also called evolutionary computation).

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