scholarly journals Neural network-based anomalous diffusion parameter estimation approaches for Gaussian processes

2021 ◽  
Author(s):  
Dawid Szarek
Keyword(s):  
Parameter Estimation ◽  
Brownian Motion ◽  
Anomalous Diffusion ◽  
Gaussian Processes ◽  
Short Term Memory ◽  
Theoretical Calculations ◽  
Estimation Method ◽  
Diffusion Parameter ◽  
Long Short Term Memory ◽  
Crowded Environments

AbstractAnomalous diffusion behavior can be observed in many single-particle (contained in crowded environments) tracking experimental data. Numerous models can be used to describe such data. In this paper, we focus on two common processes: fractional Brownian motion (fBm) and scaled Brownian motion (sBm). We proposed novel methods for sBm anomalous diffusion parameter estimation based on the autocovariance function (ACVF). Such a function, for centered Gaussian processes, allows its unique identification. The first estimation method is based solely on theoretical calculations, and the other one additionally utilizes neural networks (NN) to achieve a more robust and well-performing estimator. Both fBm and sBm methods were compared between the theoretical estimators and the ones utilizing artificial NN. For the NN-based approaches, we used such architectures as multilayer perceptron (MLP) and long short-term memory (LSTM). Furthermore, the analysis of the additive noise influence on the estimators’ quality was conducted for NN models with and without the regularization method.

scholarly journals Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 349
Author(s):  
Donghoon Shin ◽  
Beomjin Yoon ◽  
Seungryeol Yoo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Short Term Memory ◽  
Estimation Method ◽  
State Of Charge ◽  
Estimation Methods ◽  
Short Term ◽  
Term Memory ◽  
Soc Estimation ◽  
Long Short Term Memory

Many battery state of charge (SOC) estimation methods have been studied for decades; however, it is still difficult to precisely estimate SOC because it is nonlinear and affected by many factors, including the battery state and charge–discharge conditions. The extended Kalman filter (EKF) is generally used for SOC estimation, however its accuracy can decrease owing to the uncertain and inaccurate parameters of battery models and various factors with different time scales affecting the SOC. Herein, a SOC estimation method based on the EKF is proposed to obtain robust accuracy, in which the errors are compensated by a long short-term memory (LSTM) network. The proposed approach trains the errors of the EKF results, and the accurate SOC is estimated by applying calibration values corresponding to the condition of the battery and its load profiles with the help of LSTM. Furthermore, a multi-LSTM structure is implemented, and it adopts the ensemble average to guarantee estimation accuracy. SOC estimation with a root mean square error of less than 1% was found to be close to the actual SOC calculated by coulomb counting. Moreover, once the EKF model was established and the network trained, it was possible to predict the SOC online.

scholarly journals Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Christoph Dinh ◽  
John G. Samuelsson ◽  
Alexander Hunold ◽  
Matti S. Hämäläinen ◽  
Sheraz Khan
Keyword(s):  
Short Term Memory ◽  
Epileptiform Activity ◽  
Estimation Method ◽  
Short Term ◽  
Norm Estimates ◽  
State Response ◽  
Source Estimation ◽  
Auditory Steady State Response ◽  
Long Short Term Memory ◽  
Past Activity

Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.

scholarly journals Remaining Useful Life Estimation for Systems with Abrupt Failures

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Wei Huang ◽  
Hamed Khorasgani ◽  
Chetan Gupta ◽  
Ahmed Farahat ◽  
Shuai Zheng
Keyword(s):  
Short Term Memory ◽  
Failure Time ◽  
Estimation Method ◽  
Remaining Useful Life ◽  
Short Term ◽  
Mode Detection ◽  
Useful Life ◽  
Long Short Term Memory ◽  
Early Degradation

Data-driven Remaining Useful Life (RUL) estimation for systems with abrupt failures is a very challenging problem. In these systems, the degradation starts close to the failure time and accelerates rapidly. Normal data with no sign of degradation can act as noise in the training step, and prevent RUL estimator model from learning the degradation patterns. This can degrade RUL estimation performance significantly. Therefore, it is critical to identify degradation mode during the training step. Moreover, in the application step, predicting RUL when the system is in normal mode and is not showing any sign of degradation can generate inaccurate estimations, and reduce faith in the model. In this paper, we propose a new RUL estimation method that incorporates an early degradation mode detection step to automatically identify the earliest point of time at which the degradation starts to happen. When the degradation mode is detected, a Long Short Term Memory (LSTM) neural network is applied to predict system RUL. As a case study, we apply the proposed method for RUL estimation in 2018 PHM Data Challenge. The case study demonstrates that our solution achieves more accurate RUL estimation compared to several baseline methods.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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