A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic con...

A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy classical recent approaches accelerator magnets, th...

The paper discusses the applicability of approximate NARX models of nonlinear dynamic systems to model based nonlinear control. The models might be obtained by a new version of Fourier analysis based neural network. The proposed controller is based on a discrete-time model of the plant. The objective is to incorporate plant modelling and control design into a unified framework where on the one ...

Solar Radiation (SR) is one of the most important parameters in the design of photovoltaic systems (PVs). An accurate evaluation of the SR of a given location is essential for the efficient design and utilization of PVs. In this paper, a nonlinear autoregressive recurrent neural networks with exogenous input (NARX) was used to predict the SR in Mutah city. Hourly, weather data of three variable...

A novel prediction scheme is proposed for real-time MPEG video to predict the burst and long-range dependent traffic. The trend and periodic characteristics of MPEG video traffic are fully captured by a proposed stochastic state-space dynamic model. Then a recursive filtering algorithm is proposed to estimate traffic for long-range prediction. Simulation results based on real MPEG traffic data ...

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then us...