Fuzzy Logic Using Matlab
To probability theory fuzzy logic has a weak linkage. In Bayesian framework the probabilistic methods which deals in imprecise knowledge are framed. Fuzzy logic using matlab acts as a useful tool for controlling and running of systems and industrial processes.
Feature of FUZZY LOGIC USING MATLAB are:
- Standard Mamdani and sugeno type fuzzy inference systems.
- Support AND,OR and NOT logic.
- Membership functions for creating fuzzy inference systems.
- To embed a fuzzy inference system in a simulink model.
- Building fuzzy inference system , viewing and analyzing results by using fuzzy logic design app.
2015 IEEE FUZZY LOGIC USING MATLAB
- Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.
- Critic-Based Self-Tuning PI Structure for Active and Reactive Power Control of VSCs in Microgrid Systems.
- An Alternative Node Deployment Scheme for WSNs.
- Theory of Generalized Fuzzy Discrete-Event Systems.
- Adaptive Fuzzy Output Feedback Dynamic Surface Control of Interconnected Nonlinear Pure-Feedback Systems.
- Medical Data Compression and Transmission in Wireless Ad Hoc Networks.
- State and Output Feedback Control of A Class of Fuzzy Systems with Mismatched Membership Functions.
- Coordinated Control Strategy of Wind Turbine Generator and Energy Storage Equipment for Frequency Support.
- Adaptive Tracking Control for A Class of Nonlinear Systems With a Fuzzy Dead-Zone Input.
- Wavelet Fuzzy Neural Network With Asymmetric Membership Function Controller for Electric Power Steering System via Improved Differential Evolution.
Characteristics of fuzzy logic using matlab systems are
- In complex information decision making along with specified values are allowed in fuzzy logic
- In failure of conventional logic fuzzy logic takes its role and deals with any expressed natural language
- Required for uncertain or nearby reasoning for a system of mathematical model which is difficult to derive makes use of fuzzy logic using matlab.
Uses of fuzzy system
- Automotive systems.
- Consumer electronics.
- Environmental controls.
- Domestic goods.
The elements that are needed in control process of fuzzy logic:
Definitions of fuzzy sets, fuzzy rules, fuzzy logic operators, inference mechanism and computing numerical values of output signals.
The five GUI tools for building, observing, editing fuzzy systems in fuzzy logic toolbox are
Membership function editor.
Fuzzy inference system editor.
Advantages that are offered under cognitive uncertainty,computational neural networks to the theory of fuzzy logic are
- Fault tolerance.
Features of fuzzy logic using matlab are
- A complete help online system.
- Collection of pre-defined mathematical functions.
- For plotting and displaying of data two –and three – dimensional graphics.
- Use of advanced algorithms for performance numerical computation for matrix algebra.