## Introduction to Fuzzy Logic

It is an approach of reasoning to make decisions by the humans which involve digital value yes or no. It uses a fuzzy set with a fuzzy logic computer process using natural language. They are applied in rule-based automatic controllers establishes non-linear mapping and considered to be a designed method by the consumers. This system works on the principle based on the probability of input state a particular output is been assigned. The word fuzzy means precision to imprecision. It comprises four components namely fuzzifier, rules, inference engine, defuzzifier. In fuzzy true statements become a matter of degree.

### Definition

It is defined as a control logic that pretends to use degrees of input and output to estimate human reasoning with the integration of rule-based implementation. The technique used in the manipulation of undesired information or facts which involves some degree of uncertainty.

### Understanding

Before understanding, it is good to know basic terms to go ahead. Linguistic variables are the input or output variables whose values are words, not a number. examples temperature (t) as a variable. Next, membership functions are the context-dependent use to map the non-fuzzy values. for instance, as an example of temperature variables, the membership functions include:

T (temperature)={cold , hold ,warm}

It has connectives like conjunctions and disjunctions for the degree of implicitly. And has a set of inference rules to make a decision statement. They help to represent some kind of human expertise.

### How does it make Working so Easy?

In some scenarios, mathematical models are constructed to process non-linear systems. In recent days the focus is to achieve them at lower costs which then led to renewed fuzzy logic. Several iterations steps are required by much-complicated systems to find a set of rules. It works with neural networks to make clustering set as easy as possible. The concept of fuzzy logic is based on human reasoning that’s why it makes the job quite easier.

### What can you do with Fuzzy Logic?

It is a rule-based phenomenon and relies on artificial intelligence software which is also considered as a subset of AI. They can be applied to engineering applications and appeared in non- engineering applications like medical diagnosing, stock application, in general, they are applied almost to all systems which have input and output logic.

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### Working

It works on the concepts of sets and the output decisions are based on the assumptions. The fuzzy set has a range of values of {0,1}. They work based on fuzzy rules namely if-then rule. Reasoning in it is the most important matter which gives 1 for the true value and 0 for a false value. And imprecision for partial values. All these fuzzy actions grouped together to form a system output. For instance, let’s take an example:

- Is Saturday a weekend day? the fuzzy answer is 1, true.
- Is Sunday a weekday? the fuzzy answer is 0.9.

Firstly, the set of input data is grouped and transformed into a fuzzy set using membership functions and linguistic terms. This process is termed as Fuzzification. Based on the set of rules organized an inference is made. Lastly, during the defuzzification step, the yielded fuzzy output is mapped to crisp output with the help of membership functions.

For example in air conditioning system fuzzy logic system plays a role by declaring linguistic variables for temperature, defining membership sets (0,1) and the set of rules through the process of fuzzification crisps the fuzzy set and the evaluation like AND, OR operation rule is done by the inference engine and finally the desired output is converted into non-fuzzy numbers using defuzzification.

### Advantages

- It recognizes work on commercial and practical approaches.
- They are headed for its simplicity and flexibility and applied to various engineering products. It helps to construct non-linear functions.
- Algorithms and mathematical concepts behind the fuzzy system are easy and very natural.
- It simplifies conventional system implementation and their work is easy to understand.

### Why Should We Use Fuzzy Logic?

It is more understandable and easier to use in many technologies. They generalize binary distribution termed as the degree of possibility. People in industry sectors and academics delegates are exploring the fuzzy logic necessities and its related technologies to solve an uncertain information approach. It is recently advent by the researches as they are well suited for control system applications which helps in weather and earthquake forecasting in China.

#### Scope

Nowadays you could find fuzzy logic systems in applications like videography, telecommunications, washing -machines, cars, air – conditioning. they have been widely used in channels equalization, signal detection, signal processing. There are some areas where it plays a vital role in automotive systems televisions, hi-fi, microwave ovens. In aerospace, they are implemented for satellite control, altitude decision in spacecraft.

#### Why do we need it?

It helps in implementing Artificial Intelligence. They act as a decision support tool for managers in the business sector. It helps in computing Natural Language with imprecise probabilities. All the fuzzy logic real-applications involves linguistic variables (the concept of precision). They have high power in formulating of fields such as medicine and other conflict situations.

#### How this tool will help you in Career Growth?

It is always exciting to work with the latest and good technologies to solve business problems. The impact of it on employment goes high. There is an ever-growing market for professionals who indulge themselves in learning fuzzy concepts. You could find a career in the specialization field of machine learning, Artificial Intelligence and mathematics fields These professionals find their role in manufacturing, machines, finance, and accounting.

### Conclusion

In conclusion, we have done with the basics of this system and working on it. As this will benefit you to understand the scope of fuzzy in a real-time environment. It is likely to scatter their nature in visibility and acceptance. They took a wide place in research and development; precisely these applications can be found in mathematical and scientific works. There are also numerous other applications where it has to be developed. In the coming years, it plays an important role in the development of real-life applications.

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