Comparison Between Non-Linear Autoregressive and Non-Linear Autoregressive with Exogeneous Inputs Models for Predicting Cardiac Ischemic Beats
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The prediction accuracy and generalization ability of neural models for forecasting Myocardial Ischemic Beats depends on type and architecture of employed network model. This paper presents the comparison analysis of recurrent neural network (RNN) architectures with embedded memory, Non-linear Autoregressive (NAR) and Non-linear Autoregressive with Exogeneous inputs (NARX) models for forecasting Ischemic Beats in ECG. Numerous architectures of the NAR and NARX models are verified for prediction and the performances are evaluated in terms of MSE. The performances of NAR and NARX models are validated by using ECG signals acquired from MIT-BIH database. The results have depicted that the NARX architecture with 2 neurons in hidden layer and 1 delay line outperformed with least Mean Square Error (MSE) of 0.0001 for detecting the ischemic beats in ECG.We develop optimal formulations for nonlinear autoregressive models by representing them as linear autoregressive models with time-varying temporal dependence coefficients. We propose a parameter updating scheme based on the score of the predictive likelihood function at each time point. The resulting time-varying autoregressive model is formulated as a nonlinear autoregressive model and is compared with threshold and smooth-transition autoregressive models. We establish the information theoretic optimality of the score driven nonlinear autoregressive process and the asymptotic theory for maximum likelihood parameter estimation. The performance of our model in extracting the time-varying or the nonlinear dependence for finite samples is studied in a Monte Carlo exercise. In our empirical study we present the in-sample and out-of-sample performances of our model for a weekly time series of unemployment insurance claims.
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This study investigates an application of nonlinear autoregressive (NAR) models to the prediction of the most likely time series of emotional state transitions of speakers engaged in dyadic conversations. While, previous methods analyzed each speaker in separation, the new approach proposes to couple both speakers into a nonlinear recursive predictive neural network system (NARX-NN). The NARX-NN system was tested and compared with its uncoupled version (NAR-NN). The tests were conducted using speech recordings from 63 parent-child dyads including 29 depressed and 34 non-depressed adolescent children, 14-18 years of age. The conversations were conducted on three different topics. The NARX-NN outperformed the NAR-NN method in all experimental scenarios and across all topics of conversation. Predictions of emotional states for depressed children led to higher accuracy than the predictions for non-depressed children. Modeling with class and/or speaker dependency improved the results compared to the class and/or speaker independent models.
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Abstract A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine’s dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.
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In our work, we compared two approaches for predicting changes in the concentration of one of the main greenhouse gases - methane. The study is based on surface methane concentration data obtained by monitoring the dynamics of changes in major greenhouse gases on the Arctic Island Belyy, Russia. We used a nonlinear autoregressive neural network with an external input (NARX), and a vector regression model. An artificial neural network type NARX was more accurate for predicting methane concentration changes.
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우리나라 전력시장 발전부문에 경쟁을 도입함으로써 각발전사들이 얼마나 발전해야 할지는 중요한 문제가 되었고, 안정적인 계통한계가격(System Marginal Price, SMP)유지는 중요한 문제다. 본 연구에서는 인공신경망 중 MLP(Multi-Layer Perceptron), NARX(Nonlinear Autoregressive exogenous)를 이용하여 서로 비교 분석했고 외부변수로 전력수요를 설정하여 SMP를 예측했으며, ARIMA(Autoregressive Integrated Moving Average)와도 비교를 실시했다. 모두 30일, 40일, 50일을 예측해 봤으며 MLP의 경우 은닉층의 개수가 90개, 85개, 80개 였을 때 최적의 결과를 보여준 반면, NARX의 경우 95개, 80개, 70개 였을 때 최적의 결과를 보여주었다. 전반적으로 모든 예측에서 ARIMA, MLP, NARX 순으로 예측 오차가 작게 나타났다.
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Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive with exogenous input (ARX) and autoregressive moving average with exogenous input (ARMAX)]. The models were validated using experimental data from full-scale plants. Simulation results revealed that the performance of the NARX neural network is better compared to the ARMAX and ARX. The NARX neural network may serve as a valuable forecasting tool for the plants.
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Large deep foundation pits are usually in a complex environment, so their surface deformation tends to show a stable rising trend with a small range of fluctuation, which brings certain difficulty to the prediction work. Therefore, in this study we proposed a nonlinear autoregressive exogenous (NARX) prediction method based on empirical wavelet transform (EWT) pretreatment is proposed for this feature. Firstly, EWT is used to conduct adaptive decomposition of the measured deformation data and extract the modal signal components with characteristic differences. Secondly, the main components affecting the deformation of the foundation pit are analyzed as a part of the external input. Then, we established a NARX prediction model for different components. Finally, all predicted values are superpositioned to obtain a total value, and the result is compared with the predicted results of the nonlinear autoregressive (NAR) model, empirical mode decomposition-nonlinear autoregressive (EMD-NAR) model, EWT-NAR model, NARX model, EMD-NARX model and EWT-NARX model. The results showed that, compared with the EWT-NAR and EWT-NARX models, the EWT-NARX model reduced the mean square error of KD25 by 91.35%, indicating that the feature of introducing external input makes NARX more suitable for combining with the EWT method. Meanwhile, compared with the EMD-NAR and EWT-NAR models, the introduction of the NARX model reduced the mean square error of KD25 by 78.58% and 95.71%, indicating that EWT had better modal decomposition capability than EMD.
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The prediction accuracy and generalization ability of neural models for forecasting Myocardial Ischemic Beats depends on type and architecture of employed network model. This paper presents the comparison analysis of recurrent neural network (RNN) architectures with embedded memory, Non-linear Autoregressive (NAR) and Non-linear Autoregressive with Exogeneous inputs (NARX) models for forecasting Ischemic Beats in ECG. Numerous architectures of the NAR and NARX models are verified for prediction and the performances are evaluated in terms of MSE. The performances of NAR and NARX models are validated by using ECG signals acquired from MIT-BIH database. The results have depicted that the NARX architecture with 2 neurons in hidden layer and 1 delay line outperformed with least Mean Square Error (MSE) of 0.0001 for detecting the ischemic beats in ECG.
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The results of the prediction of a model based on an artificial neural network were compared to predict the concentration of carbon dioxide (CO2) in the surface layer of the atmosphere for different time intervals. Measurements were taken on the Arctic island of Belyy, Russia. For comparison, three time intervals were used, which differed in the dependence of carbon dioxide concentration on the time of day. A non-linear autoregressive neural network with external input (NARX) was used. The model based on NARX successfully coped with the prediction. The smallest error was for the time intervals with a strong dependence of CO2 concentration on the time of day.
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The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly of autoregressive approaches. Autoregression, the usage of the model outputs of previous time steps, is a method of transferring a system state between time steps, which is not necessary for modeling dynamic systems with modern neural network structures, such as gated recurrent units (GRUs) and Temporal Convolutional Networks (TCNs). We compare the accuracy and execution performance of autoregressive and non-autoregressive implementations of a GRU and TCN on the simulation task of three publicly available system identification benchmarks. Our results show, that the non-autoregressive neural networks are significantly faster and at least as accurate as their autoregressive counterparts. Comparisons with other state-of-the-art black-box system identification methods show, that our implementation of the non-autoregressive GRU is the best performing neural network-based system identification method, and in the benchmarks without extrapolation, the best performing black-box method.
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