this paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. the uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. the contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

G4-FET has attracted attention as an emerging device for the future generations of semiconductor industry. This paper is intended to propose a model representing the characteristics of G4FET device in order to perform circuit simulations. The modeling approach is established upon the neuro-fuzzy technique whose main strength is that they are universal approximators with the ability to solicit i...

Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some refinements, they may not, in general, be good approximators for adaptive systems. By extending fuzzy sets to qualitative fuzzy sets, fuzzy logic may become universal approximators for adaptive systems. Similar considerations can be extended to neural networks.

Neural Networks and Fuzzy Inference Systems are becoming well-recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or timevarying nature of the system un...

Function and its partial derivative approximation based upon a set of discrete dataset are important issues in soft computing. Several function approximators have been presented most of them fits a model to the dataset so that the Mean Squared Error is minimized. In this paper, we propose to calculate the derivative of the Neuro-Fuzzy function approximator directly according to the parametric s...

Both Takagi–Sugeno (TS) and Mamdani fuzzy systems are known to be universal approximators. In this paper, we investigate whether one type of the fuzzy approximators is more economical than the other type. The TS fuzzy systems in this study are the typical two-input single-output TS fuzzy systems: they employ trapezoidal or triangular input fuzzy sets, arbitrary fuzzy rules with linear rule cons...

This paper connects two thoroughly investigated universal approximator techniques to each other. Recently, it has been shown that the input-output function of the general fuzzy KH interpolation method [1, 2] as well as its modification [3] are stable in the mathematical sense, or in other words, they can be considered as universal approximators with respect to the Lp (p ∈ [1,∞]) norm in the spa...

In a great diversity of knowledge areas, the variables that are involved in the behavior of a complex system, perform normally, a non-linear system. The search of a function that express those behavior, requires techniques as mathematics optimization techniques or others. The new paradigms introduced in the soft computing, as fuzzy logic, neuronal networks, genetics algorithms and the fusion of...