Fuzzy Logic Matlab Projects.
Fuzzy logic which is abbreviated as FL is a significant methodology in the field of soft computing.Fuzzy logic projects are developed using Matlab simulation tool. It is an easy version of standard set theory, as it works under fuzzy set theory. FL is also widely known as a superset of Boolean logic which is elaborated in order to hold the concept of partial truth. These matlab ideas are specially designed for image understanding applications like clustering, Edge detection, classification and feature extraction. This logic is applied to solve problems of classification in the field of image processing. In the field of medicine, detection of diseases by examining medical images will be done using this methodology. Fuzzy logic is also called as problem solving control system.
Fuzzy Control System.
Fuzzy control rules: Fuzzy control rules are framed on the basis of experience of human knowledge. The knowledge of human mind is revealed according to the chosen application. Fuzzy rule is the expertise of human knowledge. These rules are divided into fuzzy implication rule and fuzzy mapping rule.
Fuzzy implication rule: Fuzzy implication rule is described via the relation of logic implication between inputs and outputs. This rule is most relevant to classical techniques of two valued logic and multiple valued logic.
Fuzzy mapping rule: Fuzzy mapping rules make use of linguistic variables in giving functional mapping among input and output. It always provides good results in real applications.
We Assist Research Scholars in Implementing Fuzzy Logic Matlab Projects.
Applications of Fuzzy Logic Projects.
Many applications are involved in FL.
- Hospitals.
- Libraries.
- Automobile Productions.
- Banks.
- Academic education.
- Automatic control.
- Industrial manufacturing.
To apply fuzzy logic in real life applications, three main techniques are needed. They are fuzzification, fuzzy inference process and defuzzification.
Through fuzzification any crisp data can be transformed into a fuzzy data or membership functions. By using both membership functions and controlled rules, Inference process derives the fuzzy output. This aids in approximate reasoning and it also infers the control of action. Through the outputs of signal which then is projected in a table format called as lookup table.
Fuzzy logic Matlab System:
FL works by using membership function of the set theory, which makes it to achieve a sharp input which leads to a logical output. Every fuzzy set has three variables such as input1, input2 and output. If
Input1: checks inputs quality of service like good, poor and excellent.
Input 2: checks inputs quality of food such as awful and delicious.
Output: outputs are classified into low, medium and high.
Advantages of Fuzzy Logic Matlab System.
- Mimics human control logic.
- Modified and tweaked easily.
- Fails safely.
- Use imprecise language.
- Inherently robust.
Fuzzy Matlab set:
Fuzzy set also known as crisp set uses as its base a galaxy of discourse to consider the set of membership in an infinite manner. Certain degree of fuzzy membership can be formed as a function of membership values.
Features of membership functions:
- Boundary.
- Normal fuzzy set.
- Convex fuzzy set.
- Core.
- Cross over points.
- Support.
Uncertainty: Uncertainty is parallel to that of human’s ability to comprehend illegible handwriting, stammer in speech and the little nuances in languages. Uncertainty can also be called as a measure of entropy. There are two types of uncertainty they are stochastic uncertainty and lexical uncertainty.
Structure of fuzzy logic
- Fuzzification – Transforming input variables to fuzzy sets
- Inference mechanism – Reduce control action, Approximate learning
- Defuzzification – Conversion of output value to signal
Edge Detection process to be done with the help of fuzzy logic. Input pixel values are splitted using window mask algorithm.
Membership function can be created based on triangular membership function.Mamdani rules are applied for edge detection process.
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