We present the RWiener package that provides R functions for the Wiener diffusion model. The core of the package are the four distribution functions dwiener, pwiener, qwiener and rwiener, which use up-to-date methods, implemented in C, and provide fast and accurate computation of the density, distribution, and quantile function, as well as a random number generator for the Wiener diffusion mode...

In this paper, we consider Wiener nonlinear model for solid oxide fuel cell (SOFC). The Wiener model of the SOFC consists of a linear dynamic block and a static output non-linearity followed by the block, in which linear part is approximated by state-space model and the nonlinear part is identified by a polynomial form. To control the SOFC system, we have to consider various view points such as...

In order to exhaustively exploit the high-level capabilities of shape memory alloys (SMAs), they must be applied in control systems applications. However, because of their hysteretic inherent, dilatory response, and nonlinear behavior, scientists are thwarted in their attempt to design controllers for actuators of such kind.  The current study aims at developing a micro-position control system ...

This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal problems based on such are generally non-convex and difficult solve online. In this paper, we propose a model that combines Hammerstein-Wiener with input convex neural networks, which have recently been proposed in field learning. An important feature is resulting effectiv...

This paper presents a two-dimensional Wiener model in spatial domain for a class of laser electrophotographic printers, and embeds it into a framework of model-based digital halftoning. The Wiener model comprises an FIR filter followed by a nonlinear static mapping. The nonlinear static mapping is synthesized using a fuzzy system to maximize its capability to approximate nonlinearity of high de...

The performance of Wiener filters in restoring the quality and intelligibility of noisy speech depends on: (i) the accuracy of the estimates of the power spectra or the correlation values of the noise and the speech processes, and (ii) on the Wiener filter structure. In this paper a Bayesian method is proposed where model combination and model decomposition are employed for the estimation of pa...

In this paper, we consider a robust output feedback model predictive controller(MPC) design for Wiener model. Nonlinearities that couldn’t be represented in static nonlinearity block of Wiener model are regarded as uncertainties in linear block. An dynamic output feedback controller design method is presented for Wiener MPC. According to MPC algorithm, the control law is computed based on linea...

This paper presents a detailed performance analysis of thirdorder nonlinear adaptive systems based on the Wiener model. In earlier work, we proposed the discrete Wiener model for adaptive filtering applications for any order. However, we had focused mainly on first and second-order nonlinear systems in our previous analysis. Now, we present new results on the analysis of third order systems. Al...

In a number of adaptive filtering applications, non-Wiener effects have been observed for the (normalized) leastmean-square algorithm. These effects can lead to performance improvements over the fixed Wiener filter with the same model structure, and are characterized by dynamic behavior of the adaptive filter weights. Here we investigate whether such non-Wiener effects can also occur in the rec...