The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive...
Neural Networks are largely used in a vast number of applications, including time series prediction, function approximation, pattern classification. Recently Nonlinear Auto Regressive with eXogenous input (NARX) Recurrent Neural Networks has been used in to predict noisy and large time series (also referred as chaotic time series). This paper present a multiobjective optimized implementation of NARX...
Model predictive control (MPC), also known as receding horizon control (RHC), is an advanced control strategy for optimizing the performance of control systems. For nonlinear systems, standard MPC schemes based on linearization would result in poor performance. In this paper, we propose an MPC scheme for nonlinear affine systems based on a recurrent neural network (RNN) called the simplified dual...
This paper presents Model predictive control (MPC) of nonlinear hybrid system based on neural network (NN) optimization. Multiple model method is used to modeling of nonlinear hybrid system and these models are combined using Bayes theorem. NN optimization combined gradient NN with recurrent NN is proposed to solve optimization problem of each sample time in MPC. An example of benchmark three spherical...
This paper presents a discrete-time recurrent neural network (RNN) model for solving nonlinear differentiable constrained optimization problems, which contain the special case of convex optimizations over constrained sets and variational inequality problem. The qualitative analysis results about the regularity and completeness of the proposed network have been obtained. It is shown that all trajectories...
In this paper, a recurrent neural network for both convex and nonconvex equality-constrained optimization problems is proposed, which makes use of a cost gradient projection onto the tangent space of the constraints. The proposed neural network constructs a generically nonfeasible trajectory, satisfying the constraints only as t rarr infin. Local convergence results are given that do not assume convexity...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.