What is fuzzy logic in Artificial intelligence

You can't always decide in your daily life simply by answering "Yes" or "No." This is due to the possibility of encountering circumstances in which there is insufficient data to make a judgment. Or perhaps we are a little perplexed ourselves. For instance, you are unlikely to respond Yes or No right away if someone approaches you if you are accessible on a specific date in the following month. It's because you're not 100 percent certain you won't be booked on that specific date the following month. Isn't that difficult? Artificial intelligence uses fuzzy logic to help computers handle comparable circumstances when the supplied data is a little hazy. 
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Table of Content:
How come fuzzy logic?
Functioning of Fuzzy Logic:
How does fuzzy logic work?
Cloud Computing Architecture:
Fuzzy logic has advantages in artificial intelligence:
Disadvantages of Fuzzy Logic in Artificial Intelligence:

How come fuzzy logic?

The benefits of the fuzzy logic controller are implied by the following statements.

• Fuzzy logic is frequently employed in practice and business contexts.
• Machines and consumer products can be controlled by it.
• It may not provide exact logic, but it provides adequate reasoning.
• It addresses the ambiguity in engineering.

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Functioning of Fuzzy Logic:

Sets are at the core of fuzzy logic's operation. The sets make a variety of assumptions, and the result may be exactly calculated first from corresponding assumed values. "If-else" sets are the primary configuration used in fuzzy logic. Additionally, the many input variants will be used to get a precise response. Considerations with "if-else" provide precise output vavalueThe inputs that are divided into different memberships when fuzzy logic is used in artificial intelligence to get the results. The "if-else" logic allocates inputs based on membership. When using fuzzy logic in systems with multiple inputs, there are a variety of input variables. Consequently, the AND operation will be used on the supplied input variables to calculate the system output.

Due to the inclusion of several components including a controller, fuzzifier, and defuzzifier, this logic functions as a conventional controller on a few systems. For instance, the room temperature affects how the automatic cooling system regulates temperature. As a result, different room temperatures are taken into account as the input, and the controlled output is produced by fuzzy logic. The output of a fuzzy logic controller is dependent on the likelihood of the state of the input. It operates on the output's judgment, which is predicated on an assumption. It makes advantage of fuzzy sets. Language-related variables referencing the output state are included in each set.

Let's use an illustration to clarify fuzzy logic. Assume that A is a subgroup of X and that each element of A exists between 0 and 1. Thus, set A is referred to as a fuzzy set. The degree to which X is a member of the set determines the output. Additionally, the membership function is used to graphically display the fuzzy sets. Be aware that the output is dependent on both the condition of the input and the rate at which it is changing.

How does fuzzy logic work?

Fuzzy refers to something t bit hazy. The computer might not be capable to come up with a response that is either True or False when the circumstance is ambiguous. Boolean logic states that a value of 1 denotes True and a value of 0 denotes False. However, a fuzzy logic approach takes into account all of a problem's unknowns, where other alternative values beyond True or False may exist. Artificial intelligence's fuzzy logic uses various levels of input possibilities to produce definite results. It can run on a variety of devices, including tiny microcontrollers to massive, networked, workstation-centered control systems, with diverse capabilities and sizes. It can also be carried out using hardware, software, or a hybrid of both. Lotfi Zadeh coined the phrase "fuzzy logic" in 1965. He believed that complex or ambiguous data cannot be handled by conventional computer logic. Similar to humans, computers are capable of incorporating a wide range of True and False values. These include:

• Without a doubt,
• perhaps yes
• Can't say perhaps not
• not

Cloud Computing Architecture:

These are the elements that make up the fuzzy logic architecture:
2) Fuzzification:                                                                                                                                                                                                                   

Crisp numbers are transformed into fuzzy sets in this stage. A group of items with the same characteristics is known as a crisp set. An item can either be a part of the set or not based on some logic. Crisp sets use binary logic, which only accepts Yes/No responses. Here, a normalized fuzzy subset is created by converting the error signals with physical values. The fuzzifier in any fuzzy logic system divides the input signals among five states, which are:

• hugely favorable
• mid-level positive
• Medium Small Negative
• massively negative

Crisp inputs from sensors, like room temperature, are converted throughout the fuzzification process and sent to the control scheme for additional processing. Fuzzy Logic is the foundation of a fuzzy logic control system. Fuzzy Control systems are found in common household goods like air conditioners and washing machines.

3) Engine of Inference:

The degree to which the input values as well as the rules match is determined by the inference engine. By the input values obtained, the rules are then applied. Then, control actions are created using the rules. In a fuzzy logic system, the controller is the collective name for the inference engine and knowledge base.

4) Defuzzification:

This is the opposite of the fuzzification process. Here, mapping is used to turn the fuzzy values into crisp values. There are various defuzzification techniques available for this, however, the best one is chosen based on the input. Methods like the maximum membership principle, total average method, and centroid method are used in this difficult process.

Fuzzy logic has advantages in artificial intelligence:

The following are some advantages of employing fuzzy logic systems:

• Minimal precise inputs are necessary because it is a reliable system.
• These systems can accept a variety of inputs, including hazy, skewed, or imprecise data.
• You can reprogramme the feedback sensor if it stops functioning in case of an emergency.
• The Fuzzy Logic methods can be coded with less information, taking up less memory.
• These systems can solve complicated issues with unclear inputs and make conclusions as a result because their reasoning resembles human reasoning.
• These frameworks are adaptable, as well as the rules can indeed be changed.
• The systems are easily constructible and have a straightforward structure.
• As these systems can handle cheap sensors, you can reduce system costs.
• It is simple to comprehend.
• By improving its capacity to carry out tasks that require decision-making and reasoning similar to those performed by humans, it effectively resolves complicated problems.
• It addresses engineering uncertainties.
• Due to the fuzzy logic's adaptability, FLS can be modified more easily by merely adding or removing rules.

Disadvantages of Fuzzy Logic in Artificial Intelligence:

Let us look at the drawbacks of Fuzzy Logic systems:

• The accuracy of these systems is compromised as the system mostly works on inaccurate data and inputs
• There is no single systematic approach to solving a problem using Fuzzy Logic. As a result, many solutions arise for a particular problem, leading to confusion
• Due to inaccuracy in results, they are not always widely accepted
• A major drawback of Fuzzy Logic control systems is that they are completely dependent on human knowledge and expertise
• You have to regularly update the rules of a Fuzzy Logic control system
• These systems cannot recognize machine learning or neural networks

A form of thinking that mirrors human reasoning is fuzzy logic (FL). In response to incomplete, confusing, distorted, or erroneous (fuzzy) information, it produces an acceptable but clear output.

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