Modelling and prediction of machining errors using ARMAX and NARMAX structures

Fung, Emily; Wong, Y.K.; Ho, H.F.; Mignolet, Marc P.

doi:10.1016/s0307-904x(03)00071-4

Cited by 47 publications

(24 citation statements)

References 15 publications

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“…This model is a generalisation of the ARMAX (AutoRegressive Moving Average with eXogenous inputs) model, designed for linear systems (Akaike, 1969, 1970, Fung et al, 2003.…”

Section: The Narmax Identification Methodologymentioning

confidence: 99%

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Florescu¹

2017

Springer Theses

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To my family iii The third problem addressed in this work is given by the need of developing neuromorphic engineering circuits that perform mathematical computations in the spike domain.In this respect, this thesis developed a new representation between the time encoded input and output of a linear filter, where the TEM is represented by an ideal IF neuron. A new practical algorithm is developed based on this representation. The proposed algorithm is significantly faster than the alternative approach, which involves reconstructing the input, simulating the linear filter, and subsequently encoding the resulting output into a spike train.

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“…This model is a generalisation of the ARMAX (AutoRegressive Moving Average with eXogenous inputs) model, designed for linear systems (Akaike, 1969, 1970, Fung et al, 2003.…”

Section: The Narmax Identification Methodologymentioning

confidence: 99%

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Florescu¹

2017

Springer Theses

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“…Equation (2) is the linear difference equation of an ARMAX (n, m, r) (orders of the model), with y(k) being the output to compute, u the exogenous (X) variable or system input, and e is the moving average (MA) variable ( (Fung et al, 2003)). In the case of this work, temperature T SoC is the output y(k) to be estimated, and the system input is…”

Section: Temperature Modelmentioning

confidence: 99%

Modular Modelling of an Embedded Mobile CPU-GPU Chip for Feature Estimation

Djedidi

Djeziri

M'Sirdi

et al. 2017

Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics

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Abstract:This paper deals with the modelling of a CPU-GPU chip embedded in an Android phone. The model is used for the estimation of variables that characterise the operating state of System on Chip (SoC). The proposed model is built to demonstrate the causal relationships between the variables, through its interconnected structure of subsystems. This structure allows the extension of other components or the easy exchange of subsystems in the case of a change in components or operating mode. The model developed here requires no additional instrumentation-other than the one present on the phone-which facilitates its implementation. It is used for the estimation of the state of the system and can also be used for monitoring and behaviour prediction. The model is validated and the results are promising for further implementation.

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“…Identification strategies of various kinds by means of input-output measurements are commonly used in many situations in which it is not necessary to achieve a deep mathematical knowledge of the system under study, but it is sufficient to predict the system evolution. [14,15] This is often the case in control applications, where satisfactory predictions of the system that are to be controlled and sufficient robustness to parameter uncertainty are the only requirements. In chemical systems, parameter variations and uncertainty play a fundamental role on the system dynamics and are very difficult to be accurately modeled.…”

Section: Input-output Modeling Approachmentioning

confidence: 99%

“…[13,14] 1. Assignŵ T (k ) to the input vector x T (k ) and apply it to the input units whereŵ T (k ) is the regression vector given by the following equation:…”

Section: Calculation Of the Nn Outputmentioning

confidence: 99%

Using ARX and NARX approaches for modeling and prediction of the process behavior: application to a reactor‐exchanger

Chetouani

2008

Asia-Pacific J Chem Eng

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Chemical industries are characterized often by nonlinear processes. Therefore, it is often difficult to obtain nonlinear models that accurately describe a plant in all regimes. The main contribution of this work is to establish a reliable model of a process behavior. The use of this model should reflect the normal behavior of the process and allow distinguishing it from an abnormal one. Consequently, the black-box identification based on the neural network (NN) approach by means of a nonlinear autoregressive with exogenous input (NARX) model has been chosen in this study. A comparison with an autoregressive with exogenous input (ARX) model based on the least squares criterion is carried out. This study also shows the choice and the performance of ARX and NARX models in the training and test phases. Statistical criteria are used for the validation of the experimental data of these approaches. The identified neural model is implemented by training a multilayer perceptron artificial neural network (MLP-ANN) with input-output experimental data. An analysis of the inputs number, hidden neurons and their influence on the behavior of the neural predictor is carried out. In order to illustrate the proposed ideas, a reactor-exchanger is used. Satisfactory agreement between identified and experimental data is found and results show that the neural model predicts the evolution of the process dynamics in a better way. 

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Modelling and prediction of machining errors using ARMAX and NARMAX structures

Cited by 47 publications

References 15 publications

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Modular Modelling of an Embedded Mobile CPU-GPU Chip for Feature Estimation

Using ARX and NARX approaches for modeling and prediction of the process behavior: application to a reactor‐exchanger

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