The gradient algorithm for parameter and output estimation for dual-rate CARARMA systems ⋆
نویسندگان
چکیده
A recursive generalized extended stochastic forgetting gradient algorithm is used to identify the dual-rate stochastic systems based on the polynomial transformation technique. A time-varying forgetting factor is included to improve the rate of convergence. The intersample output estimation algorithm is also studied in the paper. Finally, a simulation example shows that the algorithm is excellent effective in parameter identification and output estimation.
منابع مشابه
A Gradient Based Adaptive Control Algorithm for Dual-rate Systems
In this paper, using a polynomial transformation technique, we derive a mathematical model for dual-rate systems. Based on this model, we use a stochastic gradient algorithm to estimate unknown parameters directly from the dual-rate input-output data, and then establish an adaptive control algorithm for dual-rate systems. We prove that the parameter estimation error converges to zero under pers...
متن کاملADAPTIVE NEURO FUZZY INFERENCE SYSTEM BASED ON FUZZY C–MEANS CLUSTERING ALGORITHM, A TECHNIQUE FOR ESTIMATION OF TBM PENETRATION RATE
The tunnel boring machine (TBM) penetration rate estimation is one of the crucial and complex tasks encountered frequently to excavate the mechanical tunnels. Estimating the machine penetration rate may reduce the risks related to high capital costs typical for excavation operation. Thus establishing a relationship between rock properties and TBM pe...
متن کاملLeast-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems
This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive IN-CAR model and the input nonlinear controlled autoregressive autoregressive moving average IN-CARARMA model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algori...
متن کاملSECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کاملParameter Estimation of Loranz Chaotic Dynamic System Using Bees Algorithm
An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Bees algorithm (BA) is a new member of me...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008