http://www.control.isy.liu.se/research/reports/2003/2480.pdf

Identification of piecewise affine systems via mixed-integer programming

A blind approach to the Hammerstein-Wiener model identification

http://liu.diva-portal.org/smash/get/diva2:608221/FULLTEXT01.pdf

Identification of Hammerstein-Wiener models

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.

/pdf/comprehensive-review-of-artificial-neural-network-4sb2zr5avn.pdf

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

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.

A Blind Approach to the Hammerstein-Wiener Model Identification

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.

Alex D. Kalafatis

Papers

Identification of Wiener-type nonlinear systems in a noisy environment

Linearizing feedforward-feedback control of pH processes based on the Wiener model

Identification of time-varying pH processes using sinusoidal signals

Design of an On-Line Titrator for Nonlinear pH Control