Real-time Discrete Nonlinear Identification via Recurrent High Order Neural Networks

نویسندگان

  • Alma Y. Alanis
  • Edgar N. Sánchez
  • Alexander G. Loukianov
  • Marco A. Pérez Cisneros
چکیده

This paper deals with the discrete-time nonlinear system identification via Recurrent High Order Neural Networks, trained with an extended Kalman filter (EKF) based algorithm. The paper also includes the respective stability analysis on the basis of the Lyapunov approach for the whole scheme. Applicability of the scheme is illustrated via real-time implementation for a three phase induction motor.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Real-time Discrete Nonlinear Identification via Recurrent High Order Neural Networks Identificación No Lineal en Tiempo Real usando Redes Neuronales

This paper deals with the discrete-time nonlinear system identification via Recurrent High Order Neural Networks, trained with an extended Kalman filter (EKF) based algorithm. The paper also includes the respective stability analysis on the basis of the Lyapunov approach for the whole scheme. Applicability of the scheme is illustrated via real-time implementation for a three phase induction motor.

متن کامل

Real-time Discrete Nonlinear Identification via Recurrent High Order Neural Networks Identificación No Lineal en Tiempo Real usando Redes Neuronales Recurrentes de Alto Orden

This paper deals with the discrete-time nonlinear system identification via Recurrent High Order Neural Networks, trained with an extended Kalman filter (EKF) based algorithm. The paper also includes the respective stability analysis on the basis of the Lyapunov approach for the whole scheme. Applicability of the scheme is illustrated via real-time implementation for a three phase induction motor.

متن کامل

Discrete-Time Backstepping Synchronous Generator Stabilisation Using a Neural Observer

This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of bounded disturbances, based on a neural observer. A high order neural network structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF); besides the observer is based on a recurrent high-order neural network (RHONN), which es...

متن کامل

Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

متن کامل

Multivariable Adaptive Control Using a Recurrent Neural Network

An indirect adaptive controller based on a hybrid solution is presented and applied. Using data concerning the process, a dynamic recurrent neural network (Elman’s type) is trained to model a general nonlinear discrete time system. By assuming a linearisation of this neural model, a time varying standard linear state space model is derived. As a consequence, some well-established conventional t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Computación y Sistemas

دوره 14  شماره 

صفحات  -

تاریخ انتشار 2010