نتایج جستجو برای: narx recurrent neural network

تعداد نتایج: 942763  

Journal: :Neurocomputing 2008
José Maria P. Menezes Guilherme De A. Barreto

The NARX network is a dynamical neural architecture commonly used for inputoutput modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward Time Delay Neural Network (TDNN), i.e. without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architectur...

Journal: :amirkabir international journal of modeling, identification, simulation & control 2014
a. fakharian r. mosaferin m. b. menhaj

in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

Journal: :IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 1997

S.Samavi, V. Tahani and P. Khadivi,

Routing is one of the basic parts of a message passing multiprocessor system. The routing procedure has a great impact on the efficiency of a system. Neural algorithms that are currently in use for computer networks require a large number of neurons. If a specific topology of a multiprocessor network is considered, the number of neurons can be reduced. In this paper a new recurrent neural ne...

2016
SANAM NAREJO

Weather forecasting is a challenging time series forecasting problem because of its dynamic, continuous, data-intensive, chaotic and irregular behavior. At present, enormous time series forecasting techniques exist and are widely adapted. However, competitive research is still going on to improve the methods and techniques for accurate forecasting. This research article presents the time series...

Journal: :desert 2011
m.t. dastorani h. afkhami

in recent decades artificial neural networks (anns) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. this paper presents the application of artificial neural networks to predict drought in yazd meteorological station. in this research, different archite...

S.Samavi, V. Tahani and P. Khadivi,

Routing is one of the basic parts of a message passing multiprocessor system. The routing procedure has a great impact on the efficiency of a system. Neural algorithms that are currently in use for computer networks require a large number of neurons. If a specific topology of a multiprocessor network is considered, the number of neurons can be reduced. In this paper a new recurrent neural ne...

2014
A. Di Piazza M. C. Di Piazza G. Vitale

This paper is focused on the prediction and forecast of climate time series, particularly useful for planning and management of the power grid, by artificial neural networks. An appropriate prediction and forecast of climate variables, indeed, improves the overall efficiency and performance of renewable power plants connected to the power grid. On such a basis, the application of suitable Artif...

2015
Martin Macaš Fabio Moretti

In this paper, an extension of sensitivity based pruning (SBP) method for Nonlinear AutoRegressive models with eXogenous inputs (NARX) model is presented. Besides the inputs, input and output delays are simultaneously pruned in terms of the backward elimination. The concept is based on replacement of some regressors by their mean value, which corresponds to the removal of influence of the parti...

Journal: :JCP 2011
Chuanjin Jiang Fugen Song

Chaotic time-series is a dynamic nonlinear system whose features can not be fully reflected by Linear Regression Model or Static Neural Network. While Nonlinear Autoregressive with eXogenous input includes feedback of network output, therefore, it can better reflect the system’s dynamic feature. Take annual active times of sunspot as an example, after verifying the chaos of sunspot time-series ...

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