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Alex D. Kalafatis

Bio: Alex D. Kalafatis is an academic researcher from Aspen Technology. The author has contributed to research in topics: Linearization & Recursive least squares filter. The author has an hindex of 3, co-authored 4 publications receiving 264 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the frequency response for the linear subsystem and the inverse of the static nonlinearity were used to identify Wiener systems in a noisy environment, where a variety of input excitation signals, including random binary and periodic, can be used with the proposed method.
Abstract: Wiener systems are widely encountered in engineering and science applications. This paper proposes a straightforward approach to the identification of Wiener systems in a noisy environment. The obtained models are represented in terms of the frequency response for the linear subsystem and the inverse of the static nonlinearity. A variety of input excitation signals, including random binary and periodic, can be used with the proposed method. Simulation studies using a pH neutralization continuous stirred tank reactor show that excellent results are obtained with relatively short data lengths.

146 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the control of pH processes based on the Wiener model construct (a dynamic linear element representing the mixing dynamics of the process in series with a static nonlinearity representing the titration curve).

66 citations

Journal ArticleDOI
TL;DR: This paper presents an approach to the identification of time-varying, nonlinear pH processes based on the Wiener model structure that produces an on-line estimate of the titration curve, where the shape of this static nonlinearity changes as a result of changes in the weak-species concentration and/or composition of the process feed stream.

55 citations

Journal ArticleDOI
TL;DR: In this article, a new on-line titrator for pH control has been developed, which generates an estimate of the inverse titration curve as a continuous smooth function eliminating the need for selection of the important break points of the titration curves.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper provides algorithms based on mixed-integer linear or quadratic programming which are guaranteed to converge to a global optimum of hybrid dynamical systems, and suggests a way of trading off between optimality and complexity by using a change detection approach.

384 citations

Journal ArticleDOI
Er-Wei Bai1
TL;DR: By using the blind approach, it is shown that all internal variables can be recovered solely based on the output measurements and identification of linear and nonlinear parts can be carried out.

274 citations

Journal ArticleDOI
TL;DR: A new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures is developed and illustrated that addresses the blind Wiener estimation problem as a special case.

250 citations

Journal ArticleDOI
TL;DR: There is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems and the research focus on current models and the development of new models concurrently for more successes in the field.
Abstract: The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

217 citations

Journal Article
TL;DR: In this paper, a blind approach to the sampled Hammerstein-Wiener model identification is proposed, where no a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.
Abstract: In this paper, we propose a blind approach to the sampled Hammerstein-Wiener model identification. By using the blind approach, it is shown that all internal variables can be recovered solely based on the output measurements. Then, identification of linear and nonlinear parts can be carried out. No a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.

190 citations