This paper presents the formulation of a nonlinear model identification method based on recurrent neural network (RNN) Nonlinear AutoRegressive with eXternal input (NARX) model derived from dynamic feedforward neural network (DFNN) by adding feedback connection between output and input layers. The proposed identification method identifies the neural network (NN) model of an input-output system....

In this paper we evaluate the use of system identification methods to build a thermal prediction model heterogeneous SoC platforms that can be used quickly predict temperature different configurations without need hardware. Specifically, focus on modeling approaches based clock frequency and utilization percentage each core. We investigate three with respect their accuracy: linear state-space a...

An accurate state of charge (SOC) estimation depends on an battery model. The influence nonlinear and unstable interference factors makes the SOC difficult. To obtain model, a method based NARX (nonlinear autoregressive network with exogenous inputs) recurrent neural moving window is proposed. This paper improves accuracy, modelling speed robustness from following three aspects. First, to overc...

In this paper, we present a recurrent neural network model for solving CCR Model in Data Envelopment Analysis (DEA). The proposed neural network model is derived from an unconstrained minimization problem. In the theoretical aspect, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to the optimal solution of CCR model. The proposed model has...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir’s state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who ...

--------------------------------------------------ABSTRACT-------------------------------------------------------This study aims to investigate suitable model and forecast future wheat price using backpropagation neural network (BPNN) and nonlinear autoregressive models with exogenous inputs (NARX) networks. The price of wheat was estimated using prices of 3 types of grains widely used in agric...

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers th...