# Fuzzy logic project titles

This work is concerned with the development of information retrieval system. In the end, the results are indexed according to these queries and the documents retrieved. Different retrieval systems have been researched over time which may include vector space, Boolean, probabilistic etc.

Zadeh to enhance the precision of information retrieval in archives. Most information retrieval systems have their different retrieval method which might include weight matching, probability etc. But this work utilizes the concept of membership function and fuzzy set theory.

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The reason why this method was selected was due to the fact that archives usually contain large document and sometimes the user might not have a perfect idea of what he want to retrieve. The results were then compared to other search and matching systems like the Lucene App developed with Java and does not use fuzzy logic, the Rubens App which uses fuzzy logic and Doc Fetcher retrieved from the internet used for searching files and documents. The model proposed in this work proved far more effective than the aforementioned such that some of the aforementioned software above produced congested results or none at all.

Logic in its literal meaning could mean the ability of a system to make a rational decision which can be regarded as the theory of reasoning in decision making. Unfortunately, Boolean logic has its limitations.

### Free research papers and projects on fuzzy logic

This is due to the fact that it is limited to a set of 0, 1 only, meaning Boolean logic is too precise. This limitation is being eliminated by the concept of fuzzy logic. Fuzzy logic is a branch of logical systems and artificial intelligence. Fuzzy logic is a kind of logic that allows for imprecise or ambiguous answers to questions, forming the basis of computer programming designed to mimic human intelligence Microsoft Encarta Encyclopedia, Unlike Boolean logic, fuzzy logic extends its set elements to [0.

From the above, it could be seen that fuzzy logic compared to Boolean logic, is more complex and it is not too precise, giving a wider range of results to a condition. Rather than mere producing true of false, fuzzy logic can produce very true, true, false, very false. It is typically needed in fuzzy control systems. Defuzzification is interpreting the membership degrees of the fuzzy sets into a specific decision or real value.

Fuzzy set theory defines fuzzy operations on fuzzy sets. Therefore, fuzzy logic uses IF — Then — Else constructs in the format:. These operators are defined as:. Fuzzy logic has being applied in many areas which include; medicine, engineering equipment, databases, archives, etc.

Data of archives usually consists of compressed files having extensions either. Archives mostly contain very old files that are not needed for daily processing but only for reference purposes. Though, archiving has advantages, organizations cannot archive as they please.The term fuzzy refers to things which are not clear or are vague. In this way, we can consider the inaccuracies and uncertainties of any situation. In boolean system truth value, 1. But in the fuzzy system, there is no logic for absolute truth and absolute false value.

But in fuzzy logic, there is intermediate value too present which is partially true and partially false. Definition: A graph that defines how each point in the input space is mapped to membership value between 0 and 1.

Input space is often referred as the universe of discourse or universal set uwhich contain all the possible elements of concern in each particular application.

There are largely three types of fuzzifiers:. This article is contributed by Surya Priy, Abhishek rajput. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute. See your article appearing on the GeeksforGeeks main page and help other Geeks. Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below. Writing code in comment? Please use ide. What Should I Do? Improved By : sheikhSaiSreevani.

Load Comments.Fuzzy Logic Systems FLS produce acceptable but definite output in response to incomplete, ambiguous, distorted, or inaccurate fuzzy input. Fuzzy Logic FL is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. It can be implemented in systems with various sizes and capabilities ranging from small micro-controllers to large, networked, workstation-based control systems.

Membership functions allow you to quantify linguistic term and represent a fuzzy set graphically. Here, each element of X is mapped to a value between 0 and 1. It is called membership value or degree of membership. It quantifies the degree of membership of the element in X to the fuzzy set A. There can be multiple membership functions applicable to fuzzify a numerical value.

Simple membership functions are used as use of complex functions does not add more precision in the output. The triangular membership function shapes are most common among various other membership function shapes such as trapezoidal, singleton, and Gaussian. Hence the corresponding output also changes. Let us consider an air conditioning system with 5-level fuzzy logic system. This system adjusts the temperature of air conditioner by comparing the room temperature and the target temperature value. Linguistic variables are input and output variables in the form of simple words or sentences.

For room temperature, cold, warm, hot, etc. Every member of this set is a linguistic term and it can cover some portion of overall temperature values. Create a matrix of room temperature values versus target temperature values that an air conditioning system is expected to provide. Fuzzy set operations perform evaluation of rules. Combine all results of evaluation to form a final result.

This result is a fuzzy value. Fuzzy logic is a solution to complex problems in all fields of life, including medicine, as it resembles human reasoning and decision making. Previous Page. Next Page. Previous Page Print Page.Imhotep: A user-centric approach to adaptable mobile interfaces. Fuzzy Maximum Likelihood Classifier for single class extraction from an image with multiple classes. Android application for the diagnosis of cardiovascular diseases using Fuzzy Logic. Coursework: Computation Theory.

Fuzzy logic recognise and name actual colour with its degree and brightness. Simple Fuzzy logic implementation using fuzzylite with mamdani method. SonarQube Sedcat plugin for quality measurement in unit testing. Add a description, image, and links to the fuzzy-logic topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the fuzzy-logic topic, visit your repo's landing page and select "manage topics.

Learn more. Skip to content. Here are 41 public repositories matching this topic Language: Java Filter by language. Sort options. Star Code Issues Pull requests. Updated Oct 9, Java. Star 5. Complete Java Approach.

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### fuzzy-logic

Star 3. Neural network and fuzzy logic based plugins for imagej. Updated Sep 6, Java. Updated Jan 20, Java. Updated Nov 12, Java. Star 2.Now it might be doubled or 10 times more than that count. It is a fourth generation programming language and multi-paradigm numerical computing environment. MATLAB is widely used in image processing, signal processing, academic and research institutions as well as industrial enterprises.

Another important point is that most of the MATLAB users come from various backgrounds like science, engineering and economics. We have already got good idea about the following engineering projects:. All these projects are collected from various resources and are very useful for engineering students.

This list will be updated regularly. You may write your queries and suggestions regarding this list in contact us page. It is used in electric vehicles for providing better balancing in curved paths. It is lighter in weight than mechanical differential and has an embedded system to supervise the electronics. Even though there are many models to solve this, the Automatic Car Parking System presented here could be used for efficient usage of parking space. The concept of image processing is used for inspecting objects.

Using this application, the pattern or geometry of an object can be detected. This feature can be useful in developing computer vision applications where we do not have to take measurements manually or physically. Based on this, a simple authentication algorithm is developed to check whether the sender and the receiver are authenticated. The Voltage Multiplier containing switched capacitor and coupled inductors, obtains high step-up gain.

This inverter is designed for photovoltaic grid connected power generation systems. A Hybrid Median Filter for Noise Removal in Digital Images : The main aim of this project is to remove impulse noises that cause corruption of images during storing and processing of captured images.

So this project makes use of hybrid median filter in order to preserve the main image features. Fuzzified Particle Swarm Optimization : This project aims to design an optimized fuzzy logic controller with the use of particle swarm optimization for achieving greater extent of DC motor speed control.Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive.

It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. The term fuzzy logic was introduced with the proposal of fuzzy set theory by Lotfi Zadeh. Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or sets are mathematical means of representing vagueness and imprecise information hence the term fuzzy.

These models have the capability of recognising, representing, manipulating, interpreting, and utilising data and information that are vague and lack certainty. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. Classical logic only permits conclusions which are either true or false. However, there are also propositions with variable answers, such as one might find when asking a group of people to identify a color.

In such instances, the truth appears as the result of reasoning from inexact or partial knowledge in which the sampled answers are mapped on a spectrum.

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Both degrees of truth and probabilities range between 0 and 1 and hence may seem similar at first, but fuzzy logic uses degrees of truth as a mathematical model of vaguenesswhile probability is a mathematical model of ignorance. A basic application might characterize various sub-ranges of a continuous variable. For instance, a temperature measurement for anti-lock brakes might have several separate membership functions defining particular temperature ranges needed to control the brakes properly.

Each function maps the same temperature value to a truth value in the 0 to 1 range. These truth values can then be used to determine how the brakes should be controlled. While variables in mathematics usually take numerical values, in fuzzy logic applications, non-numeric values are often used to facilitate the expression of rules and facts.

A linguistic variable such as age may accept values such as young and its antonym old. Because natural languages do not always contain enough value terms to express a fuzzy value scale, it is common practice to modify linguistic values with adjectives or adverbs.

For example, we can use the hedges rather and somewhat to construct the additional values rather old or somewhat young. Fuzzification operations can map mathematical input values into fuzzy membership functions.

And the opposite de-fuzzifying operations can be used to map a fuzzy output membership function into a "crisp" output value that can be then used for decision or control purposes. Fuzzification is the process of assigning the numerical input of a system to fuzzy sets with some degree of membership. This degree of membership may be anywhere within the interval [0,1]. If it is 0 then the value does not belong to the given fuzzy set, and if it is 1 then the value completely belongs within the fuzzy set.

Any value between 0 and 1 represents the degree of uncertainty that the value belongs in the set.

## Artificial Intelligence - Fuzzy Logic Systems

These fuzzy sets are typically described by words, and so by assigning the system input to fuzzy sets, we can reason with it in a linguistically natural manner.

For example, in the image below the meanings of the expressions coldwarmand hot are represented by functions mapping a temperature scale. A point on that scale has three "truth values"—one for each of the three functions. The vertical line in the image represents a particular temperature that the three arrows truth values gauge.

Since the red arrow points to zero, this temperature may be interpreted as "not hot"; i. The orange arrow pointing at 0. Therefore, this temperature has 0. The degree of membership assigned for each fuzzy set is the result of fuzzification.

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Fuzzy sets are often defined as triangle or trapezoid-shaped curves, as each value will have a slope where the value is increasing, a peak where the value is equal to 1 which can have a length of 0 or greater and a slope where the value is decreasing. Fuzzy logic works with membership values in a way that mimics Boolean logic. There are several ways to this.The goal of fuzzy logic is formalization of modes of reasoning that are more or less exact to near values.

Fuzzy system has its beginning in ancient Greek philosophy. It is an option to traditional of set membership and logic. The theory of fuzzy logic begins with fuzzy set. A set where its boundary is not clearly defined is fuzzy set.

The method of equating the mapping from an input to output using fuzzy logic is fuzzy inference. The membership functions are connected with terms which appear in antecedents or consequents of rules. In rules based application of fuzzy logic objective of membership functions:.

Singleton pattern supports a global point of access to assure a class only at one place. From compiles time type inference Runtime singleton argument is determined. Various way to meet or control and classify problem are stated in fuzzy logic. The operations that operate or units below the level of words are lexical operations.

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There must be no manifestation or syntactical stratum or signs. A road map of whole fuzzy inference process is displayed in rule viewer. Each column is a variable and every rule is a low of plots. Fuzzy logic toolbox and Simulink in Matlab software is being used to construct a simulation system of fuzzy logic controller for water tank level control by applying simulation package. In most of the systems it is highly complex where the attitude can be laid by set of rules that are imprecise and depend on linguistic terms laden with uncertainty.

Fuzzy logic based projects are supported to academic students. We update the fuzzy logic based projects title from the Elsevier journal which has high impact factor by make use of current technologies.

Fuzzy Logic Based Projects The goal of fuzzy logic is formalization of modes of reasoning that are more or less exact to near values. In rules based application of fuzzy logic objective of membership functions: Easy to calculate and fit to the problem With parameters that are meaningful, with membership and operators closely connected On a theoretical basis Easy to calculate and fit to the problem With a linearised form for the applications Singleton pattern supports a global point of access to assure a class only at one place.