Nonlinear autoregressive network with exogenous inputs based contour error reduction in CNC machines

Huo, Feng; Poo, A.N.

doi:10.1016/j.ijmachtools.2012.12.007

Cited by 39 publications

(13 citation statements)

References 11 publications

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“…An exogenous autoregressive model (NARX) aimed at the identification of non-linear systems involves both current and past values of the impulsive series that models the dynamics of the system. NARMAX (Nonlinear Autoregressive Moving Average with Exogenous Inputs) methods provide models that are transparent, and easily solve many problems such as contour errors in CNC machines, forecasting, network traffic, and prediction of daily solar radiation [ 38 , 39 , 40 , 41 ]. The NARMAX model is defined in Equation ( 16 ).…”

Section: Materials and Methodsmentioning

confidence: 99%

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops

Jimenez-Sierra

Correa

Benítez-Restrepo

et al. 2021

Sensors

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Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.

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Section: Materials and Methodsmentioning

confidence: 99%

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops

Jimenez-Sierra

Correa

Benítez-Restrepo

et al. 2021

Sensors

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“…The nonlinear autoregressive network with exogenous inputs (NARX) is an important class of a dynamic network which encompasses the two features above, i.e., a time-delayed nonlinear input (x)-output (y) relationship (NX) and the auto-regressive properties of the output Y (AR) [39,56,57]. The general input-output formulation of a NARX model is as follows:…”

Section: Nonlinear Autoregressive Neural Network With Exogenous Inputmentioning

confidence: 99%

“…(x)-output (y) relationship (NX) and the auto-regressive properties of the output Y (AR) [39,56,57]. The general input-output formulation of a NARX model is as follows:…”

Section: Nonlinear Autoregressive Neural Network With Exogenous Inputmentioning

confidence: 99%

Rainfall Prediction with AMSR–E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

Fereidoon

Koch

2018

Water

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Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR–E) generated by implementing the standard National Aeronautics and Space Administration (NASA) algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in south-west Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into training (1 January 2003 to 31 September 2005) and testing (1 September 2005 to 30 September 2006) stages. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR–E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the coefficient of determination R2, the RMSE and the statistical bias. For the SM2RAIN method, R2 ranges between 0.32 and 0.79 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. Moreover, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN–CDR) is employed to evaluate its potential for predicting the ground-based observed station rainfall, but it is found to work poorly. In conclusion, the results of the present study show that with the use of AMSR–E soil moisture products in the physically based SM2RAIN algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be predicted satisfactorily.

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“…Network (NARXNN) Model. Nonlinear autoregressive with external input (NARX) is a modified NAR model by including another relevant time series as extra input to the forecasting model [19][20][21], which can be expressed as…”

Section: Nonlinear Autoregressive With External Input Neuralmentioning

confidence: 99%

Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models

Guo

Xue

2014

Mathematical Problems in Engineering

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Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

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Nonlinear autoregressive network with exogenous inputs based contour error reduction in CNC machines

Cited by 39 publications

References 11 publications

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops

Rainfall Prediction with AMSR–E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models

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