A Markov chain approach to identification of the Wiener, Hammerstein, and nonlinear ARX (NARX) systems is presented. The motivation of this approach comes from the fact that these classes of nonlinear systems are connected with Markov chains, and hence their asymptotical properties, such as ergodicity, stationarity, and invariant probability distribution, can be derived from the corresponding c...

The applicability of approximate NARX models of non-linear dynamic systems is discussed. The models are obtained by a new version of Fourier analysis-based neural network also described in the paper. This constitutes a reformulation of a known method in a recursive manner, i.e. adapted to account for incoming data on-line. The method allows us to obtain an approximate model of the non-linear sy...

Simple Recurrent Networks (SRNs) have been widely used in natural language processing tasks. However, their ability to handle long-term dependencies between sentence constituents is somewhat limited. NARX networks have recently been shown to outperform SRNs by preserving past information in explicit delays from the network’s prior output. However, it is unclear how the number of delays should b...

Abstract—This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identificati...

در این کار پژوهشی، نمونه‌های هیدروکسی اپتایت آلاییده با ناخالصی‌های سریم، گادولونیم و ترکیبی از دو ناخالصی به روش هیدروترمال سنتز شدند. منظور بررسی فازهای بلوری تشکیل یافته هرکدام شده، کد MAUD که یک نرم‌افزار برای تجزیه تحلیل ساختار ماده استفاده پراش بر اساس ریتولد است، شد. پاسخ دزیمتری ترمولومینسانس نمونه‌ها مورد قرار گرفت یافته‌ها نشان داد نمونه‌ ترکیبی، سطح میانی است. مشخص شد افزودن سریم می‌...

â  abstract: in this paper, artificial neural network (ann) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. the actual input-output data of the system were measured in order to be used for system identification based on root mean square error (rmse) minimization approach. it was shown that the designed recurrent neural network is able to pr...

System identification uses measurements of a dynamic system's input and output to reconstruct mathematical model for that system. These can be mechanical, electrical, physiological, among others. Since most the systems around us exhibit some form nonlinear behavior, system techniques are tools will help gain better understanding our surroundings potentially let improve their performance. One is...

The prevalence of batch and batch-like operations, in conjunction with the continued resurgence artificial intelligence techniques for clustering classification applications, has increasingly motivated exploration applicability deep learning modeling feedback control processes. To this end, present study seeks to evaluate viability general, neural networks particular, toward process via a case ...

System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using input/output observations such with or without prior knowledge some system parameters. Many valid algorithms are available in literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model exogenous inputs (NARMAX) and its d...