The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the re...

The northern Gulf of Mexico coast is affected by the North Atlantic hurricane season, which causes storm surge disasters every year and brings serious economic losses to southern USA; therefore, it necessary make an accurate advance prediction level. In this paper, a model with simple structure, fast computation speed, results has been constructed based on nonlinear auto-regressive exogenous (N...

Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...

In this paper, recurrent neural network is used for path planning in the joint space of the robot with obstacle in the workspace of the robot. To design the neural network, first a performance index has been defined as sum of square of error tracking of final executor. Then, obstacle avoidance scheme is presented based on its space coordinate and its minimum distance between the obstacle and ea...

This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the nonlinear autoregressive with exogenous input model (NARX) and works according to a multi-step (MS)...

Multimedia services became a major part of the internet network traffic. The bursty characteristics of the video traffic, produced by applications like video on demand, video broadcasting or videoconferencing, make it difficult to fulfill the Quality of Service (QoS) of the multimedia applications. Therefore it is important to utilize congestion control procedures. One of the procedures used to...

Non-linear dynamical systems are difficult to control due to the model uncertainties and external disturbances that may occur in these systems. This paper addresses the problem of identification using dynamic neural networks (DNNs) based on genetic algorithm (GA) for nonlinear dynamic systems. Four different dynamic neural networks are used for identification of the same nonlinear dynamic syste...

In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the system based on neural network and time series analysis is proposed to deal one-month forecast produced power from photovoltaic modules (PVM). The PVM monocrystalline cell rated production 175 watts that placed at Heliopolis University, Bilbéis city, Egypt. NARX considered powerful enough emulate dynamic...

Foreign exchange rate prediction is a stimulating research area from past decade. There are several statistical and machine learning methods already have been proposed by the researchers for foreign exchange rate prediction which provide better results. These models performed a vital role in future financial decision making which is taken by financial department administration of that country a...