Nonlinear Autoregressive Exogenous Neural Network
Comparison between PID and Artificial Neural Networks to Control of Boiler for Steam Power Plant | Journal of Engineering Sciences
... Artificial neural network (ANN) to obtain the relationships between input and output data variables of the ...The neural network controllers are exten- sively used for their suppleness for ...
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FCM BPSO: ENERGY EFFICIENT TASK BASED LOAD BALANCING IN CLOUD COMPUTING
... (Nonlinear Autoregressive with eXogenous input) based ANN (Artificial Neural ...optimum network model according to its desired forecasting task. Network performance is analyzed ...
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Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey
... a nonlinear autoregressive model with exogenous variable in order to estimate ...use Neural Network algorithm which is developed based on the relation between neurons of the human ...
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A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis
... forward neural network ...Recurrent neural networks (RNNs) are widely used to deal with many dynamical and non-linear problems, including time series prediction ...a nonlinear ...
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A. Artificial Neural Networks
... dynamic neural network and an innovative optimized adaptive unscented Kalman filter for forecasting stock price indices of four different Indian ...dynamic neural information ...artificial ...
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Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks
... the nonlinear autoregressive with exogenous input (NARX) model with different network training methods was applied to the Index of Financial Safety (IFS) of South Africa to obtain high quality ...
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Improved Prediction of Wind Speed using Machine Learning
... artificial neural networks namely, Back Propagation Network (BPN), Radial Basis Function (RBF) and Nonlinear AutoRegressive model process with eXogenous inputs(NARX) with Mutual ...
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Predicting Turbidity in Water Distribution Trunk Mains Using Nonlinear Autoregressive Exogenous Artificial Neural Networks
... ANN network in order to assess the performance of the NARX model in comparison with the Feed-Forward network for the present case ...NARX network significantly outperforms the Feed-Forward ...
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Smart Water: Short-Term Forecasting Application in Water Utilities
... seemingly nonlinear relationship in the water demand data, this nonlinear ANN model was chosen for ...forecasting. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive ...
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Recurrent Neural Network Identification: Comparative Study on Nonlinear Process
... ABSTRACT— Neural networks (NNs) have been successfully applied to solve a variety of application problems including nonlinear modelling and ...recurrent neural networks. The most powerful types of ...
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Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modelling approach
... inherent neural hemodynamics for which no or very limited a priori information about the biophysical mechanisms (the model structure and the associated model parameters) is available, analytical or theoretical ...
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Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
... of Autoregressive Integrated Moving Average (ARIMA) or seasonal ARIMA (SARIMA), Backpropagation Neural Network (BPNN), Nonlinear Autoregressive Models with exogenous inputs ...
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RCS Calculation Using Hybrid FDTD-NARX Technique
... and nonlinear autoregressive with exogenous input (NARX) neural network to achieve a faster computation of radar cross section ...
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NARMAX_OLS Representation of a Semi-active Dynamic Leg Joint Model for a Paraplegic Subject Using Functional Electrical Stimulation
... Abstract—A nonlinear autoregressive moving average with exogenous input (NARMAX) model structure is used to develop paraplegic dynamic leg joint model and the result is compared to a model built ...
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A neural-symbolic system for temporal reasoning with application to model verification and learning
... We have chosen the NARX models to extend the MLP networks with temporal processing. As described in Chapter 2, NARX networks use recurrent links and delay units to allow the propagation of information through time. In ...
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ABSTRACT: Chaos and chaos control are new theories and new fields of nonlinear dynamics. Chaotic motion
... Artificial neural network has been proved to have the characteristics of arbitrary approximation to nonlinear ...artificial neural network with chaotic system, some scholars have ...
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Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network
... new neural network architecture called based on the adaptation of the shape of the sig- moid weight of the hidden layer neurons and have intro- duced its corresponding dynamic back propagation learning ...
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Research of adaptive control algorithm research based on rough set and implementation
... adaptive neural network algorithm has strong compatibility, some noise data, not including related function and reduce the input dimension, a fast learning process, uncertainty processing and force ...
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A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks
... Let us proceed with the implementation of the algorithm described in the previous two sections. For simplicity, let the time series of past values be composed of 50 data. This means that the nonlinear ...
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Cross validation aggregation for combining autoregressive neural network forecasts
... (Efron 1983; Kohavi 1995). The technique is used most popurlarly in out-of-sample evaluations with a single hold-out dataset (Tashman 2000) and in specific application areas, such as climate forecasting (Michaelsen ...
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