Median-Pi artificial neural network for forecasting

Eğrioğlu, Erol; Yolcu, Ufuk; Baş, Eren; Dalar, Ali Zafer

doi:10.1007/s00521-017-3002-z

Cited by 39 publications

(24 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 3 shows the DNN model constructed in this paper. DNN is an extension of an artificial neural network (ANN), with a structure that is similar to ANN but with a number of hidden layers [20][21][22][23]. Generally, neural networks that have two or more hidden layers can be regarded as a DNN.…”

Section: Establishment Of a Stator Winding Temperature Prediction Modmentioning

confidence: 99%

Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

et al. 2020

View full text Add to dashboard Cite

The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM) directly affect the its temperature rise. The accurate evaluation and prediction of stator winding temperature is of great significance to the safety and reliability of PMSMs. In order to study the influencing factors of stator winding temperature and prevent motor insulation ageing, insulation burning, permanent magnet demagnetization and other faults caused by high stator winding temperature, we propose a computer model for PMSM temperature prediction. Ambient temperature, coolant temperature, direct-axis voltage, quadrature-axis voltage, motor speed, torque, direct-axis current, quadrature-axis current, permanent magnet surface temperature, stator yoke temperature, and stator tooth temperature are taken as the input, while the stator winding temperature is taken as the output. A deep neural network (DNN) model for PMSM temperature prediction was constructed. The experimental results showed the prediction error of the model (MAE) was 0.1515, the RMSE was 0.2368, the goodness of fit (R2) was 0.9439 and the goodness of fit between the predicted data and the measured data was high. Through comparative experiments, the prediction accuracy of the DNN model proposed in this paper was determined to be better than other models. This model can effectively predict the temperature change of stator winding, provide technical support to temperature early warning systems and ensure safe operation of PMSMs.

show abstract

Section: Establishment Of a Stator Winding Temperature Prediction Modmentioning

confidence: 99%

Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

et al. 2020

View full text Add to dashboard Cite

show abstract

“…(4). The unbiased estimates of innovation ˆk r can be obtained by machine learning [26][27][28][29], such as Gaussian process regression (GPR) [30], support vector regression (SVR), neural network (NN) and so on. Users can freely choose their familiar way of algorithm to realize the fast fault detection.…”

Section: B Implementation In Navigation Systemsmentioning

confidence: 99%

A Novel Approach for Fault Detection in Integrated Navigation Systems

Zhu

Zhou

2020

IEEE Access

View full text Add to dashboard Cite

Detecting subsystem faults quickly is critical to the accuracy and reliability of integrated navigation systems. This paper, therefore, proposes an effective approach based on the novel test statistic to detect faults. Machine learning is introduced to estimate the innovation and its variance of local filter. The estimates combined with the actual ones are used to construct the test statistic, which is then proved to obey chi-square distribution. Thus fault detection can be realized by chi-square test. However, the special structure of the test statistic makes it sensitive to faults, even to the gradual faults. The experimental results demonstrate that the approach can detect faults quickly. Especially for gradual fault detection, the proposed test statistic has a marked superiority compared with the traditional test statistic of residual chi-square test.

show abstract

“…Afterward, the membrane layer performs a median function on all the branches of the dendritic layer. Because of using median operation, the prediction for time series is less affected by the outlier [22]. Therefore, the membrane layer of MDNM implements a median operation instead of the additional function.…”

Section: Membrane Layermentioning

confidence: 99%

“…Similarly, in [22], a median-pi ANN is proposed, in which median neuron model and multiplicative neuron model are simultaneously utilized to improve the performance for time series forecasting problems when the sets have outliers. The proposed ANN model is a high-order neural network and has robust architecture.…”

Section: Introductionmentioning

confidence: 99%

A Novel Median Dendritic Neuron Model for Prediction

Shi

Zou

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Dendritic neuron model (DNM) that utilizes a single dendritic neuron to emulate the information processing in human brains has been successfully applied to classification, approximation, and prediction fields. However, it is still a challenging task to improve the performance for forecasting the time series with outliers. A time series is a collection of data ordered in time. An outlier in time series as an input can affect prediction performance. Thus, in this study, we first propose a novel five median dendritic neuron model (MDNM) for prediction to deal with time series with outliers. In MDNM, DNM and median function are combined to achieve better forecasting results. Moreover, states of matter search (SMS) algorithm is used to train MDNM. Twenty real time series and a benchmark time series are adopted to verify the performance of MDNM. The comparison between the MDNM and DNM suggests that MDNM is effective for forecasting time series with outliers.

show abstract

Median-Pi artificial neural network for forecasting

Cited by 39 publications

References 28 publications

Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

A Novel Approach for Fault Detection in Integrated Navigation Systems

A Novel Median Dendritic Neuron Model for Prediction

Contact Info

Product

Resources

About