What is Soft Computing?
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, ant behavior, the warm of particles, the human nervous system, etc.
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.
Application of Soft Computing:
- 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
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). Artificial
Soft Computing Techniques:
1. Neural Networks (ANN):
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 network is an attempt to emulate a network of neurons that makes 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.
2. Fuzzy logic:
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.
3. Genetic Algorithm:
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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