AI - Fuzzy Logic Systems

https://www.cliqforex.com/attachments/1517934732.jpgArtificial ligence - Fuzzy Logic Systems
Fuzzy Logic Systems (FLS) generate acceptable but definite output in response to incomplete, ambiguous, distorted, or erroneous (fuzzy) input.
What is Fuzzy Logic?
Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL reproduces the method of decision making in humans that involves all of intermediate possibilities between electronic values YES and NO.
The conventional logic block that a computer can comprehend takes precise input and generates a definite outcome as TRUE or FALSE, which is equal to individuals YES or NO.
The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the individual decision making includes a selection of possibilities between YES and NO, such as #8722;
CERTAINLY YES POSSIBLY YES CANNOT SAY POSSIBLY NO CERTAINLY NO The fuzzy logic functions about the levels of possibilities of input to achieve the output.
Implementation
It can be implemented in systems with several sizes and capacities which range from little micro-controllers to big, networked, workstation-based control systems.
It can be implemented in hardware, appliions, or a combo of both.
Why Fuzzy Logic?
Fuzzy logic is useful for commercial and functional purposes. It can control machines and consumer products. It may not give suitable, although accurate reasoning reasoning. Logic helps to manage the uncertainty in engineering. Fuzzy Logic Systems Architecture
It's four main parts as shown #8722;
Fuzzifiion Module #8722; It transforms the machine inputs, which can be crisp amounts, into fuzzy sets. It splits the input signal into five measures for example #8722;
LP x is Large Positive MP x is Medium Positive S x is Small MN x is Medium Negative LN x is Large unfavorable Knowledge Base #8722; It stores IF-THEN rules offered by experts.
Inference Engine #8722; It simulates the human reasoning process by making fuzzy inference about the inputs and IF-THEN rules.
Defuzzifiion Module #8722; It transforms the fuzzy set obtained by the inference engine into a crisp value.

The membership functions function on fuzzy sets of variables.
Membership Function
Membership functions permit you to quantify linguistic term and reflect a fuzzy set graphically. A membership function for a set A on the universe of discourse X is described as A:X #8594; [0,1].
Here, every component of X is mapped to a value between 1 and 0. It is called membership value or level of membership. It quantifies the level of membership of this component in X to the fuzzy set A. X axis represents the universe of discourse. Y axis represents the levels of membership in the [0, 1] interval. There can be numerous membership purposes applicable to fuzzify a numerical value. Simple membership functions function as utilization of complied functions doesn't add more precision in the output.
All membership works for LP, MP, S, MN, and LN are shown as below #8722;

The triangular membership function contours are common among various other membership function shapes like trapezoidal, singleton, and Gaussian.
Here, the input to 5-level fuzzifier varies from -10 volts to 10 volts. Thus the corresponding output varies.
Example of a Fuzzy Logic System
Let us consider an ac system with 5-level fuzzy logic system. This system adjusts the temperature of air conditioner by assessing the space temperature and the target temperature value.

Algorithm Define linguistic variables and conditions. Build membership purposes for them. Build knowledge base of principles. Convert crisp information into fuzzy data collections using membership purposes. (fuzzifiion) Evaluate principles in the rule base. (port engine) Blend results from every rule. (port engine) Convert output information into non-fuzzy values. (defuzzifiion) Logic Development
Step 1: Define linguistic variables and terms
Linguistic variables are input and output variables in the kind of simple words or sentences. For room temperature, cold, hot, warm, etc., are linguistic conditions.
Temperature (t) = very-cold, cold, warm, very-warm, hot
Every member of the group is a linguistic term and it can cover some part of overall temperature values.

Step 2: Build membership purposes for them
The membership functions of temperature variable are as shown #8722;

Step3: Build knowledge base rules
Produce a matrix of space temperature values versus target temperature values that an ac system is expected to supply.

Step 4: Obtain fuzzy significance
Fuzzy set operations execute analysis of principles. The surgeries used for OR and AND are Max and Min respectively. Blend all results of test to form a final result. This result is a fuzzy value.

Step 5: Perform defuzzifiion
Defuzzifiion is then performed in accore with membership role for output.

Appliion Regions of Fuzzy Logic
The key appliion areas of fuzzy logic are given #8722;
Automotive Systems Automatic Gearboxes Four-Wheel Steering Vehicle surroundings control Consumer Electronic Goods Hi-Fi Systems Photocopiers Still and Video Cameras Television Domestic Goods Microwave Ovens Refrigerators Toasters Vacuum Cleaners Washing Machines Environment Control Air Conditioners/Dryers/Heaters Humidifiers Advantages of FLSs Mathematical theories within fuzzy logic are very straightforward. You can modify a FLS by just deleting or adding rules because of flexibility of fuzzy logic. Fuzzy logic Systems can take imprecise, distorted, noisy input info. FLSs are simple to construct and comprehend. Fuzzy logic is an alternative to complied problems in all areas of life, including mediion, as it looks human reasoning and decision making. Cons of FLSs there isn't any systematic approach to fuzzy system designing. They're understandable only when simple. They are acceptable for the problems that do not need high precision.
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I'd two SETs up to now, but I'm planning to opt more collections and soon as possible I'll post here.

Happy new year!

I use fuzzy logic in imformation technologies for probabilistic matching . Factors with higher influence are awarded high weights and whether the threshold is surpassed a choice is made. Percentages may be used .interesting but it's been superseded by artificial intelligence , the capacity to learn and configure a number of alorigithms same time and select the best one in that instance in time .