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In this paper, least-squares support vector machine (LS-SVM), whose parameters are updated by unscented Kalman filter (UKF), is adopted in the generalized predictive control (GPC) of a system with general multicompartment lung mechanics. Gaussian kernel function is employed since it presents a good approximation to the inner product of nonlinear mapping possessed in the SVM formulation. In the SVM literature, it is well known that the width parameter a of the Gaussian kernel function has an important effect on the performance. However, it is not possible to train that parameter together with the other parameters of SVM when using linear least squares. This is why we use UKF for parameter adaptation in the SVM formulation. At each time instant of the control task, all parameters of the LS-SVM model, including a, are tuned simultaneously. Another reason to employ UKF is; it avoids the suboptimal solutions caused by linearization based filters, e.g., extended Kalman filter. Due to these facts, we train the SVM model using UKF and it will be referred to as the UKF-SVM model. Simulation results concerning the application of UKF-SVM based GPC to a multicompartment lung mechanics model yields plausible performance using small amount of support vectors even when there are time-varying lung parameters and disturbance of high level affecting the system. The adopted approach can also be useful when there is not any knowledge of the system dynamics, i.e., black box. Note that, multicompartment lung mechanics system is a stand-in model that can mimic the behavior of human lung. Thus, it is appropriate for hardware-in-the-loop simulation which opens a path to the real-patient-tests of mechanical respiratory systems in the future. Copyright (C) 2020 The Authors.

In this paper, two novel approaches are proposed to improve the performance of online least squares support vector machine for classification problem. First, the parameters of support vector classifier model including kernel width parameter are simultaneously updated when a new sample arrives. In that model, kernel width parameter is a nonlinear term which cannot be estimated via least squares solution. Therefore, unscented Kalman filter is adopted to train all the parameters where Karush–Kuhn–Tucker conditions are satisfied. Second, a variable-size moving window, which is updated by an intelligent strategy, is proposed to construct the support vector set. Thus, the proposed model captures the dynamics of data quickly while precluding itself to become clumsy due to big amount of useless data. In addition, adaptive support vector set provides a lower computational load especially for the large data sets. Simultaneous training of the model parameters by unscented Kalman filter and intelligent update of support vector set provides a superior classification performance compared to the online support vector classification approaches in the literature. © 2017, Springer-Verlag GmbH Germany.

This paper proposes a novel state space least squares support vector machine (SS LS-SVM) for polynomial nonlinear state space (PNLSS) model based recursive system identification. SS LS-SVM, which also possesses an adaptive kernel function, provides an optimum formulation of the monomials (ζ) of the states and input in the PNLSS model. Hence, the PNLSS model encompasses the proposed SS LS-SVM. Recursive nonlinear state space identification is developed in the output error prediction context. The input-output observations are processed sequentially, hence leading to recursive update of the parameters using conventional Gauss-Newton optimization. System states do not need to be measured. However, to to yield a conformal representation of the actual system, number of states need to be known via some physical insight. This characterizes the identification procedure as a grey box one. The PNLSS model is employed in the generalized predictive control (GPC) of a nonlinear continuously stirred tank reactor (CSTR) system. The case which includes additive white noise on the output measurements and a time-varying parameter in the nonlinear system is considered. Numerical applications give the results of a high closed loop identification performance addition to the smooth control input and closely tracking the reference in the GPC scheme. © 2018 IEEE.

The function approximation capability of a regressor model in generalized predictive control (GPC) directly affects the tracking performance of unknown nonlinear systems. In this paper, a novel deep recurrent support vector regressor (DRSVR) is proposed as a function approximator to be adopted in the GPC scheme. This study is an extension of the authors' work [1] to the control task. The DRSVR model has a recurrent state-space structure based on the least-squares support vector regressor (LS-SVR), infinite-impulse response filter (IIR) and adaptive kernel function. The model parameters, including the Gaussian kernel width parameter σ, are updated simultaneously, providing the model to capture the time-varying system dynamics quickly. Parameters are tuned online using error-square minimization via conventional Gauss-Newton optimization while keeping the poles of the IIR filter constrained in the unit circle to maintain stability. The proposed DRSVR based GPC is applied to control nonlinear HIV dynamics. The numerical applications indicate that the proposed regressor model provides high closed loop identification performance in the GPC scheme. Hence, it provides the controller with a significant tracking capability. © 2018 IEEE.

Artificial Bee Colony (ABC) and Levenberg-Marquardt (LM) optimization algorithms are applied efficiently for nonlinear constrained and unconstrained optimization problems in literature. In this paper, an intelligent hybridization method of the ABC and LM algorithms is proposed such that their global and local exploitation superiorities are unified to reduce the computational time and escape from local minima of optimization problem. In order to prove the capability of proposed hybrid algorithm, twofold experiment is conducted. In the first phase, the hybrid algorithm is applied to optimize several nonlinear unimodal, multimodal and shifted benchmark functions. Secondly, it is applied to the constrained engineering problems and compared to literature works in several performance criteria. © 2017 Elsevier Inc. All rights reserved.

In this paper, the well-known heuristic Artificial Bee Colony algorithm (ABC) and deterministic Levenberg-Marquardt (LM) optimization method are unified to get better performance of nonlinear optimization. In the proposed cascaded ABC-LM algorithm, the power of the ABC and LM algorithms are synergized to reduce computational-time and get rid of the problem 'stucking at local minima' of some nonlinear functions. Then, the proved power of the cascaded optimization is also tested on the training of Artificial Neural Network (ANN) for classification of XOR data and nonlinear system identification of real-time inverted pendulum set-up. The comparisons in function optimization and system identification using ABC, LM and ABC-LM showed that ABC-LM optimized nonlinear functions and ABC-LM trained ANN has resulted smaller cost functions and mean-squared-error (MSE) values, respectively. © 2013 IEEE.