This paper presents the results of the use of training algorithms for recurrent neural networks
based on the extended Kalman filter and its use in electric energy price prediction, for both …

Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse
optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear …

In this paper, inverse optimal neural control for trajectory tracking is applied to glycemic
control of type 1 diabetes mellitus (T1DM) patients. The proposed control law calculates the …

In this paper, we introduce a new approach to train recurrent neurocontrollers for real-time
applications. We begin with training a recurrent neurocontroller for robustness on high …

This paper contributes by developing an univariate and multivariate harmonic
decomposition methodology to model time series. The models are stated in a state space …

Inverse optimal trajectory tracking via a control Lyapunov function (CLF) for discrete time
non-linear systems is developed and applied to type 1 diabetes mellitus patients control …

This paper deals with the blood glucose level modeling for Type 1 Diabetes Mellitus (T1DM)
patients. The model is developed using a recurrent neural network trained with an extended …

In this paper, a discrete‐time inverse optimal trajectory tracking for a class of nonlinear
positive systems is proposed. The scheme is developed for MIMO affine discrete‐time …

In this paper, we propose a high order neural network (HONN) trained with an extended
Kalman filter based algorithm to predict wind speed. Due to the chaotic behavior of the wind …

In this chapter, artificial neural networks (ANNs) inverse model is applied for estimating the
thermal performance () in parabolic trough concentrator (PTC). A recurrent neural network …