Introduction to Fuzzy Logic Projects.

Fuzzy logic can be stated as a mathematical model that solves problem in values to each data. Fuzzy logic projects are useful for students who have their interest in analyzing image processing. Modern computers use fuzzy logic techniques. Fuzzy logic can be individually applied otherwise combined with some other algorithms like genetic, association rule mining, ACO (Ant Colony Optimization). Our firm supports both hardware and software fuzzy logic projects, to research scholars and academic students. As fuzzy logic projects are simpler and elegant it has gained wide popularity. Fuzzy logic projects make use of networking system. Fuzzy logic mechanism is used in Ad hoc and sensor for identifying an optimized route. In fuzzy logic projects the fuzzy rules are generated with the help of the fuzzy inference engine from fuzzy data base.

Fuzzy Logic System.

Three steps are involved in fuzzy logic process system:

  • Fuzzification.
  • Fuzzy interface process.
  • Defuzzification.

Fuzzification: The first step involved in fuzzy logic is fuzzification, normal data can be converted into fuzzy data. In a fuzzy logic, normal data is called as crisp data.

Fuzzy interface: Fuzzy interface process contains of fuzzy interface engine, it generates control rules to derive fuzzy output.

Defuzzification: A process which is used to get relating output for each input and save them into a table is called defuzzification. The table in fuzzy logic is look up table.

Fuzzy-Logic-Projects
Fuzzy-Logic-Projects-With-Expert-Guidance

Inputs and Outputs of fuzzy logic Projects.

In fuzzy logic input and output values can be converted to linguistic values.

Fuzzy logic linguistic values:

  • Low.
  • Medium.
  • Very low.
  • High.
  • Fast.
  • Slow.
  • Very high.

To provide output of logic controller fuzzy mapping rules are used. Fuzzy logic using matlab projects are framed by using linguistic values by our experts.

Fuzzy Set Fuzzy set is improvement of classical set, where we consider the values of 1 or 0. High range values are there in fuzzy set. It provides easy range of boundaries to fuzzy numbers. The objects can be represented in various ways of ordering so fuzzy set act as powerful tool.

Fuzzy control Rule: Fuzzy rule base is related to any field of application which is represented in a sequence of IF-THEN rule.

There are 2 types of fuzzy rule:

  • Fuzzy implication rule.
  • Fuzzy mapping rule.

Applications of Fuzzy Logic Projects.

  • Electronic products.
  • Aerospace.
  • Business application.
  • Automotive.
  • Chemical Industry.

Fuzzy Logic Toolbox: The following are the functions of fuzzy logic tool box.

  • Simulink for analyzing.
  • Designing fuzzy logic system.
  • Applications for analyzing.

To fuzzy logic method the functions like fuzzy clustering and neurofuzzy can be added. To identify the system’s behavior by elegant logic rules toolbox is used.

Matlab and fuzzy logic:

In an image understanding process fuzzy logic using matlab adds a variety of applications. Feature extraction, edge detection, clustering and classification are included in Image understanding.

Image processing model can be represented by matlab tool. Automatic car parking method uses fuzzy logic which can be activated through matlab.

Fuzzy logic characteristics:

  • Simpler.
  • Very robust.
  • Cheaper to implement.
  • Easily modified.
  • Very quick process.
  • Multiple outputs and inputs.
Future scope on fuzzy logic:

In many fields fuzzy logic advancements research work is going on. For telecommunication process, micro processor based fuzzy logic control system is implemented. Traditional control system can be altered to conventional control system using fuzzy logic. We develop all the new advancement for fuzzy logic projects.

Applications of fuzzy logic with data mining concepts are intrusion detection system, medical imaging, multimedia application, education. Information retrieval from the large data base is a more important thing in data mining; here the fuzzy learning will be applied to generate the fuzzy decision tree. Fuzzy decision tree will gives more productive results.