Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

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...

New technologies have been developed and adopted to generate energy from renewable sources satisfy the increasing demand without causing environmental damage. However, estimating power output of inherently intermittent, weather-driven, non-dispatchable is a major scientific societal concern. In this study, neural network model enable short-to-middle term forecasts photovoltaic (PV) system provi...

The prevalence of batch and batch-like operations, in conjunction with the continued resurgence artificial intelligence techniques for clustering classification applications, has increasingly motivated exploration applicability deep learning modeling feedback control processes. To this end, present study seeks to evaluate viability general, neural networks particular, toward process via a case ...

This paper investigates the use of non-linear autoregressive exogenous (NARX) artificial neural networks (ANNs) to achieve black-box average dynamic models dc-dc converters capable capturing main converter non-linearities. Non-linearities may include, for example, behavior variations due changes operating point or mode (e.g., discontinuous conduction mode, continuous mode). presents design guid...

We present a reconstruction of the dynamics radiation belts from solar cycles 17 to 24 which allows us study how belt activity has varied between different cycles. The simulations are produced using Versatile Electron Radiation Belt (VERB)-3D code. VERB-3D code incorporate radial, energy, and pitch angle diffusion reproduce belts. Our use historical measurements Kp (available since cycle 17, i....

This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on autoregressive with exogenous (NARX) inputs model structure and deep convolutional fuzzy system (DCFS). The DCFS hierarchical structure, which can overcome the deficiency of general systems when facing high dimensional data. For relieving curse dimensionality, as well improving approximation perfo...

Measurement of oil and gas two-phase flow with variable regimes relies to a large extent on patterns their transitions. Using multiphase flowmeters in flows high volume fractions is therefore usually associated uncertainties. This work presents dynamic neural network method measure the rate using nonlinear autoregressive exogenous inputs (NARX). Total temperature total pressure are used as obta...

Cyber-physical energy systems provide a networked solution for safety, reliability and efficiency problems in smart grids. On the demand side, the secure and trustworthy energy supply requires real-time supervising and online power quality assessing. Harmonics measurement is necessary in power quality evaluation. However, under the large-scale distributed metering architecture, harmonic measure...