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Nonlinear system identification using a neo fuzzy neuron algorithm: electrical drive application.

R P Landim¹, B R de Menezes, S R Silva

¹Universitária Recife PE-Brasil. REGIS@NOVELL.CPDEE.UFMG.BR

International Journal of Neural Systems

|November 24, 1999

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This study introduces a novel Neo-Fuzzy-Neuron algorithm for identifying nonlinear dynamic systems. The algorithm offers efficient, on-line training for rotor flux observers in induction motor drives.

Area of Science:

Electrical Engineering
Control Systems
Artificial Intelligence

Background:

Nonlinear dynamic systems require accurate identification for effective control.
Rotor flux observers are crucial for induction motor drive performance.
Existing identification methods may have limitations in computational cost or training requirements.

Purpose of the Study:

To present a Neo-Fuzzy-Neuron algorithm for nonlinear dynamic system identification.
To apply the algorithm as a rotor flux observer for induction motor drives.
To demonstrate the algorithm's efficiency and effectiveness.

Main Methods:

Developed a Neo-Fuzzy-Neuron algorithm.
Employed on-line training with gradient descent for weight adjustment.

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Validated through simulations and experimental results.

Main Results:

The algorithm achieves on-line training with low computational cost.
Convergence in one step is mathematically proven.
Effectiveness demonstrated for flux observation in induction motor drives.

Conclusions:

The Neo-Fuzzy-Neuron algorithm is a viable tool for identifying nonlinear dynamic systems.
It provides an efficient and effective solution for rotor flux observation.
The method shows promise for real-world applications in motor drives.