Related Experiment Videos
Fuzzy identification of systems with unsupervised learning.
1Dept. of Electron., Naples Univ.
Summary
This study introduces a novel mathematical tool for creating fuzzy models. The method accurately approximates complex nonlinear functions without expert input, applicable to fuzzy logic controllers.
Area of Science:
- Computational intelligence
- Fuzzy systems engineering
Background:
- Fuzzy models require precise membership functions and consequent parameters.
- Accurate approximation of nonlinear functions is crucial in many scientific domains.
Purpose of the Study:
- To present a mathematical tool for constructing fuzzy models.
- To demonstrate a model-free function approximation system.
- To enable the development of advanced fuzzy logic controllers.
Main Methods:
- A novel mathematical approach to build fuzzy models.
- Parameter estimation from data sets for membership functions and consequents.
- Development of a complete function approximation system.
Main Results:
- The method accurately approximates any real continuous function, including strongly nonlinear ones.
- The algorithm constructs a model-free system without domain expert intervention.
- Successful application to modeling classical nonlinear functions like Rosenbrock and sine (x, y).
Conclusions:
- The proposed mathematical tool offers a robust method for fuzzy model construction.
- This model-free approach enhances the applicability of fuzzy systems.
- The algorithm has significant potential for advancing fuzzy logic controller design.
Related Concept Videos
01:26Classification of Systems-I
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
01:31Classification of Systems-II
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
01:28Methods of Classification and Identification
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
01:22Multi-input and Multi-variable systems
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
01:26Feedback control systems
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...