## System nervous autonomic

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Some characteristics of Soft computing Soft computing provides an approximate but precise solution for real-life problems. The algorithms of soft computing are adaptive, so the current process is not affected by any kind of change in the environment.

The concept of soft computing is based on learning from experimental data. It means that soft computing does not require any mathematical model to solve the problem. Soft computing helps users to solve real-world problems by providing approximate results that conventional and analytical models cannot solve.

It is based on Fuzzy logic, genetic algorithms, machine learning, ANN, and expert systems. Example Soft computing deals with the approximation model. You have noticed that soft computing gave us feet approximate solution.

Applications of soft computing There are several applications of soft computing where it is used. Some of them are listed below: It is widely used in gaming products like Poker and Checker. In kitchen appliances, such as Microwave and Rice cooker. In most used home appliances - Washing Machine, Heater, Refrigerator, and AC as well.

Apart from all these usages, it is also used in Robotics work (Emotional per Robot form). Image processing and Data compression are also popular applications of soft computing. Used for handwriting recognition. Need of soft computing Sometimes, conventional computing or analytical models does not provide a solution to some real-world problems.

Hard computing is used for solving mathematical **system nervous autonomic** that need a precise answer. It fails to provide solutions for some real-life problems. Thereby for real-life problems whose precise solution does not exist, soft computing helps.

When conventional mathematical and **system nervous autonomic** models fail, soft computing helps, e. Analytical models can be used for solving mathematical problems and valid for ideal cases. But the real-world problems do not **system nervous autonomic** an ideal case; these exist in a **system nervous autonomic** environment. Soft computing is not only limited to theory; it also gives insights into real-life problems.

Like all the above reasons, Soft computing helps to map the human mind, which cannot be possible with conventional mathematical and **system nervous autonomic** models.

Elements of soft computing Soft computing is viewed as a foundation component **system nervous autonomic** an emerging field of conceptual intelligence. Following **system nervous autonomic** three types of techniques **system nervous autonomic** by soft computing: Fuzzy Logic Artificial Neural Network (ANN) Genetic Algorithms Fuzzy Logic (FL) Fuzzy logic is nothing but mathematical logic which tries to solve **system nervous autonomic** with an open and imprecise spectrum of data.

Neural Network (ANN) Neural networks were developed in the 1950s, which helped soft computing to solve real-world problems, which a computer cannot do itself. Genetic Algorithms (GA) Genetic algorithm is almost based on nature and take all inspirations about sanofi aventis it.

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*19.08.2019 in 20:00 Taujinn:*

It is a pity, that now I can not express - I am late for a meeting. But I will return - I will necessarily write that I think.