Fuzzy Logic Using MATLAB Homework Help
Fuzzy logic is a mathematical technique of reasoning that imitates how humans make decisions. It encompasses all intermediate possibilities between No and YES (digital values). A computer only understands a conventional block of logic that takes inputs that are precise and produces TRUE or FALSE as output. This is the same as the YES or NO of humans. Fuzzy logic was invented by LoftiZadeh who observed that decision making in humans, unlike in computers includes a variety of possibilities between YES and NO. For example:
● Certainly yes
● Possibly yes
● Cannot say
● Certainly no
● Possibly no
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How is Fuzzy Logic Implemented in MATLAB?
Fuzzy logic in recent years has gained ground in several fields. Its application has significantly increased in a variety of applications from consumer products like washing machines, camcorders, cameras, etc. to decision-support systems, process control, portfolio selection, and medical instrumentation. Fuzzy logic can be seen as a logical system with an extension of multi valued logic or as a theory relating to classes of objects.
MATLAB has a Fuzzy logic toolbox where all fuzzy logic is interpreted. The linguistic variable is the basic underlying concept in FL (Fuzzy Logic). This is a variable whose values are words and not numbers. Meaning, FL can be defined as a methodology for computing with words rather than numbers. You may ask yourself “why are words used as they are inherently imprecise than numbers?” Well, fuzzy logic prefers words to numbers because words are closer to human intuition. Additionally, working with words tolerates imprecision, thus lowering the cost of solutions.
The other basic but critical concept in Fuzzy is the if-then rule. It is sometimes simply referred to as the Fuzzy rule. It has been a norm for quite a long time that rule-based systems must use Artificial Intelligence (AI). However, such systems lack the mechanism of handling fuzzy antecedents and consequent. The calculus of fuzzy rules provides this mechanism in fuzzy logic. These rules are the building blocks of the FDCL (Fuzzy Dependency and Command Language). FDCL might be effectively one of the main constituents of the Fuzzy Logic Toolbox but it is not explicitly used. Most fuzzy logic applications translate a human solution to FDCL.
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Why is Fuzzy Logic Used?
Here is a list of the most common reasons why fuzzy logic is used:
● It is conceptually simple and easy to grasp
● The logic is flexible. You do not have to start again from scratch. You can layer on more functionality on any given system
● It is imprecise data tolerant
● It can be used to model nonlinear functions that have arbitrary complexity
● It is based on a natural language
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● If-Then Rules
● Foundations of Fuzzy Logic
● Logical Operations
● Sugeno-Type Fuzzy Inference?
● Types of Fuzzy Inference Systems
● Fuzzy Inference Process
● Mamdani-Type Fuzzy Inference?
● Fuzzy Sets
● Fuzzy Clustering
● Clustering Tool
● Fuzzy C-Means Clustering
● Model Suburban Commuting Using Subtractive Clustering
● Cluster Quasi-Random Data Using Fuzzy C-Means Clustering
● Subtraction Clustering
● Data Clustering
● Simulate Fuzzy Inference Systems in Simulink
● Cart and Pole Simulation
● Membership Functions
● Anfis and the ANFIS Editor GUI
● Model Learning and Inference through ANFIS
● Neuro-Adaptive Learning
● Train Adaptive Neuro-Fuzzy Inference Systems (GUI)
● Predict Chaotic Time-Series (Code)
● Simulating Fuzzy Inference Systems Using the Fuzzy Inference Engine
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