Use of multilayer feedforward neural networks in identification and control of Wiener model

Al-Duwaish, H.; Karim, M. Nazmul; Chandrasekar, V.

doi:10.1049/ip-cta:19960376

Cited by 68 publications

(41 citation statements)

References 11 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, as a third example, we considered the fluid-flow control problem studied in [1], [15]. The input signal was a sequence u(n) = [u 1 (n) u 2 (n)] of statistically independent vectors with samples satisfying u 1 (n) = 0.5 u 2 (n) + η u (n).…”

Section: Methodsmentioning

confidence: 99%

Closed-form conditions for convergence of the Gaussian kernel-least-mean-square algorithm

Richard

Bermudez

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

View full text Add to dashboard Cite

Abstract-In addition to the choice of the usual linear adaptive filter parameters, designing kernel adaptive filters requires the choice of the kernel and its parameters. One of our recent works has brought a new contribution to the discussion about kernel-based adaptive filtering by providing the first convergence analysis of the kernel-LMS algorithm with Gaussian kernel. A necessary and sufficient condition for convergence has been clearly established. Checking the stability of the algorithm can, unfortunately, be computationally expensive because one needs to calculate the extreme eigenvalues of a large matrix, for each set of candidate tuning parameters. The aim of this paper is to circumvent this drawback by examining two easy-to-handle conditions that allow to examine how the stability limit varies as a function of the step-size, the kernel bandwidth, and the filter length. One of them is a conjectured necessary and sufficient condition for convergence that allows to greatly simplify calculations.

show abstract

Section: Methodsmentioning

confidence: 99%

Closed-form conditions for convergence of the Gaussian kernel-least-mean-square algorithm

Richard

Bermudez

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

View full text Add to dashboard Cite

show abstract

“…Firstly, a small input signal is applied to the system to ensure linear perturbation of the nonlinear system [29]. Then, the recursive least square algorithm is firstly used to identify the parameters of linear dynamic block.…”

Section: A Identification Of the Linear Blockmentioning

confidence: 99%

“…Once the parameters of the linear block are determined, the back-propagation algorithm is applied to train the feed-forward neural network such as in [29].…”

Section: A Identification Of the Linear Blockmentioning

confidence: 99%

Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

Degachi¹,

Chagra²,

Ksouri³

2018

ijacsa

View full text Add to dashboard Cite

Abstract-In this work, the pH neutralization process is described by a neural network Wiener (NNW) model. A nonlinear Model Predictive Control (NMPC) is established for the considered process. The main difficulty that can be encountered in NMPC is solving the optimization problem at each sampling time to determine an optimal solution in finite time. The aim of this paper is the use of global optimization method to solve the NMPC minimization problem. Therefore, we propose in this work, to use the Self Organizing Migrating Algorithm (SOMA) to solve the presented optimization problem. This algorithm proves its efficiency to determine the optimal control sequence with a lower computation time. Then the NMPC is compared to adaptive PID controller, where we propose to use the SOMA algorithm to formulate the PID in order to determine the optimal parameters of the PID. The performances of the two controllers based on the SOMA algorithm are tested on the pH neutralization process.

show abstract

“…Taking the expected value of both sides of (21) and using the MIA (A1) yields (22) which is the LMS mean weight behavior for an input vector .…”

Section: B Mean Weight Behaviormentioning

confidence: 99%

Stochastic Behavior Analysis of the Gaussian Kernel Least-Mean-Square Algorithm

Parreira

Bermudez

Richard

et al. 2012

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm behavior as a function of these two parameters. This paper studies the steady-state behavior and the transient behavior of the Gaussian KLMS algorithm for Gaussian inputs and a finite order nonlinearity model. In particular, we derive recursive expressions for the mean-weight-error vector and the mean-square-error. The model predictions show excellent agreement with Monte Carlo simulations in transient and steady state. This allows the explicit analytical determination of stability limits, and gives opportunity to choose the algorithm parameters a priori in order to achieve prescribed convergence speed and quality of the estimate. Design examples are presented which validate the theoretical analysis and illustrates its application.Index Terms-Adaptive filtering, kernel least-mean-square (KLMS), convergence analysis, nonlinear system, reproducing kernel.

show abstract

Use of multilayer feedforward neural networks in identification and control of Wiener model

Cited by 68 publications

References 11 publications

Closed-form conditions for convergence of the Gaussian kernel-least-mean-square algorithm

Closed-form conditions for convergence of the Gaussian kernel-least-mean-square algorithm

Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

Stochastic Behavior Analysis of the Gaussian Kernel Least-Mean-Square Algorithm

Contact Info

Product

Resources

About