The Real-Time Recurrent Learning Gradient (rtrl) algorithm is characterized by being an online learning method for training dynamic recurrent neural networks, which makes it ideal for working with non-linear control s...
详细信息
The Real-Time Recurrent Learning Gradient (rtrl) algorithm is characterized by being an online learning method for training dynamic recurrent neural networks, which makes it ideal for working with non-linear control systems. For this reason, this paper presents the design of a novel Maximum Power Point Tracking (MPPT) controller with an artificial neural network type Adaptive Linear Neuron (ADALINE), with Finite Impulse Response (FIR) architecture, trained with the rtrl algorithm. With this same network architecture, the Least Mean Square (LMS) algorithm was developed to evaluate the results obtained with the rtrl controller and then make comparisons with the Perturb and Observe (P&O) algorithm. This control method receives as input signals the current and voltage of a photovoltaic module under sudden changes in operating conditions. Additionally, the efficiency of the controllers was appraised with a fuzzy controller and a Nonlinear Autoregressive Network with Exogenous Inputs (NARX) controller, which were developed in previous investigations. It was concluded that the rtrl controller with adaptive training has better results, a faster response, and fewer bifurcations due to sudden changes in the input signals, being the ideal control method for systems that require a real-time response.
In this paper, a decision feedback recurrent neural network equalizer and a modified real time recurrent learning algorithm are proposed, and an adaptive adjusting of the learning step is also brought forward. Then, a...
详细信息
In this paper, a decision feedback recurrent neural network equalizer and a modified real time recurrent learning algorithm are proposed, and an adaptive adjusting of the learning step is also brought forward. Then, a complex case is considered. A decision feedback complex recurrent neural network equalizer and a modified complex real time recurrent learning algorithm are proposed. Moreover, weights of decision feedback recurrent neural network equalizer under burst-interference conditions are analyzed, and two anti-burst-interference algorithms to prevent equalizer from out of working are presented, which are applied to both real and complex cases. The performance of the recurrent neural network equalizer is analyzed based on numerical results.
This paper deals with a new weight-updating algorithm using Lyapunov stability theory (LST) for the training of a neural emulator (NE), of nonlinear systems, connected by an autonomous algorithm inspired from the real...
详细信息
This paper deals with a new weight-updating algorithm using Lyapunov stability theory (LST) for the training of a neural emulator (NE), of nonlinear systems, connected by an autonomous algorithm inspired from the real-time recurrent learning (rtrl). The proposed method is formulated by an inequality-constraint optimization problem where the Lagrange multiplier theory is used as the optimization tool. The contribution of this paper is the integration of the LST into the Lagrange constraint function to synthesize a new analytical adaptation gain rate satisfying the asymptotic stability of the NE and providing good emulation performances. To confirm the good performances and the convergence ability of the proposed adaptation algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.
This paper provides an adaptation algorithm for the control of complex system via recurrent neural networks. The proposed method is derived from rtrl algorithm. Neural emulator and neural controller parameters are one...
详细信息
ISBN:
(纸本)9781424481545
This paper provides an adaptation algorithm for the control of complex system via recurrent neural networks. The proposed method is derived from rtrl algorithm. Neural emulator and neural controller parameters are one-line updated independently. To illustrate the tracking and the disturbance rejection capabilities of the real time control algorithm and the efficiency of the networks parameters relaxation, an application to the large scale process: Tennessee Eastman Challenge Process (TECP) is presented.
We study the application of neural networks to modeling the blood glucose metabolism of a diabetic. In particular we consider recurrent neural networks and time series convolution neural networks which we compare to l...
详细信息
We study the application of neural networks to modeling the blood glucose metabolism of a diabetic. In particular we consider recurrent neural networks and time series convolution neural networks which we compare to linear models and to nonlinear compartment models. We include a linear error model to take into account the uncertainty in the system and for handling missing blood glucose observations, Our results indicate that best performance can be achieved by the combination of the recurrent neural network and the linear error model.
This paper presents a detailed description of the data obtained as a result of the computational simulations and experimental tests of an MPPT controller based on an ADALINE artificial neural network with FIR architec...
详细信息
This paper presents a detailed description of the data obtained as a result of the computational simulations and experimental tests of an MPPT controller based on an ADALINE artificial neural network with FIR architecture, trained with the rtrl and LMS algorithms that were used as mechanisms of control in an off-grid photovoltaic system. In addition to the data obtained with the neural control method, the data for the MPPT controller based on the traditional Perturb and Observe (P&O) algorithm are presented. The simulations were performed in MATLAB/Simulink software without using the Neural Network Toolbox for controller training. The experimental tests were performed in an open space without shaded areas, exposing the neurocontroller to varying environmental conditions. Additionally, the scripts developed in MATLAB for the neural training algorithms used in the simulations are presented. These computational simulations were structured in five test cases to represent the behavior of each controller under varying environmental conditions. The codes developed in C are part of the implementation of the MPPT neurocontroller in the PIC18F2550, from which the experimental data were obtained. The data and codes presented in this research are available in the Mendeley Data repository, which allows evaluating the performance and optimizing the training algorithms with the purpose of improving the control methods applied to photovoltaic systems. (C) 2020 The Author(s). Published by Elsevier Inc.
暂无评论