A novel online LS-SVM approach for regression and classification

In this paper, a novel online least squares support vector machine approach is proposed for classification and regression problems. Gaussian kernel function is used due to its strong generalization capability. The contribution of the paper is twofold. As the first novelty, all parameters of the SVM including the kernel width parameter σ are trained simultaneously when a new sample arrives. Unscented Kalman filter is adopted to train the parameters since it avoids the sub-optimal solutions caused by linearization in contrast to extended Kalman filter. The second novelty is the variable size moving window by an intelligent update strategy for the support vector set. This provides that SVM model captures the dynamics of data quickly while not letting it become clumsy due to the big amount of useless or out-of-date support vector data. Simultaneous training of the kernel parameter by unscented Kalman filter and intelligent update of support vector set provide significant performance using small amount of support vector data for both classification and system identification application results. © 201

Gradient-based Takagi-Sugeno fuzzy local observer

In this paper, a recently introduced nonlinear gradient-based observer [1] has been adopted for Takagi-Sugeno (TS) fuzzy systems. The designed observer is especially aimed to estimate the unmeasurable states of the TS fuzzy systems where the LMI solution is not feasible to find the observer gains. The estimation of gradient observer is evaluated based on the Levenberg-Marquardt direction where the local convergence property is guaranteed using Lyapunov function approach. The numerical simulations present accurate estimation results for TS fuzzy nonlinear systems including a comparison with the conventional Extended Kalman Filter (EKF) yielding acceptable results. © 2015 IEEE

State space ls-svm for polynomial nonlinear state space model based generalized predictive control of nonlinear systems

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

Cascaded ABC-LM algorithm based optimization and nonlinear system identification

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