On 31st December 2019 in Wuhan China, the first case of Covid-19 was reported Wuhan, Hubei province China. Soon world health organization has declared contagious coronavirus disease (COVID-19) as a global pandemic month March 2020. Since then, researchers have focused on using machine learning and deep techniques to predict future cases Covid-19. Despite all research we still face problem not h...

We investigate the capacity of a type of discrete-time recurrent neural network, called timedelay recurrent neural network, for storing spatio-temporal sequences. By introducing the order of a spatio-temporal sequence, the match law between a time-delay recurrent neural network and a spatio-temporal sequence has been established. It has been proved that the full order time-delay recurrent neura...

In this study, nonlinear autoregressive recurrent neural networks with exogenous input (NARX) were used to predict global solar radiation across New Zealand. Data for nine hourly weather variables recorded across New Zealand from January 2006 to December 2012 were used to create, train and test Artificial Neural Network (ANN) models using the Levenberg−Marquardt (LM) training algorithm, with gl...

This paper presents multi-layer feedforward neural network-based identification and approximate predictive controller (NNAPC) for a two degree-of-freedom (DOF), quarter-car servohydraulic vehicle suspension system. The nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model. A suspension travel controller is developed to improve the ride comfort and handling q...

A recurrent neural network is applied for minimizing the infinity-norm of joint torques in redundant manipulators. The recurrent neural network explicitly minimizes the maximum component of joint torques in magnitude while keeping the relation between the joint torque and the end-effector acceleration satisfied. The end-effector accelerations are given to the recurrent neural network as its inp...

In this paper, a recurrent neural network for solving linear programming problems is presented that is simpler, intuitive and fast converging. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate in this paper the MATLAB Simulink modeling and simulative verification of such a recurrent neural network. Modeling and simulative results subst...

An Artificial Neural Network (ANN), a Machine Learning (ML) modeling approach is proposed to predict the ecological state of North Lagoon Tunis, shallow restored Mediterranean coastal ecosystem. A Nonlinear Auto Regressive with exogenous input (NARX) neural network model was fitted Chlorophyll-a (Chl-a) concentrations in Tunis as an eutrophication indicator. The based on approximately three dec...