Cascade-Forward Neural Network Based on Resilient Backpropagation for Simultaneous Parameters and State Space Estimations of Brushed DC Machines

• URL: http://arxiv.org/abs/2104.04348v1
• Date: Wed, 31 Mar 2021 08:56:14 GMT
• Title: Cascade-Forward Neural Network Based on Resilient Backpropagation for Simultaneous Parameters and State Space Estimations of Brushed DC Machines
• Authors: Hacene Mellah, Kamel Eddine Hemsas, Rachid Taleb
• Abstract summary: A sensorless speed, average temperature and resistance estimation technique based on Neural Network (NN) is proposed in this paper. The main objective of this paper is to introduce an intelligent sensor based on resilient BP to estimate simultaneously the speed, armature temperature and resistance of brushed DC machines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A sensorless speed, average temperature and resistance estimation technique based on Neural Network (NN) for brushed DC machines is proposed in this paper. The literature on parameters and state spaces estimations of the Brushed DC machines, shows a variety of approaches. However, these observers are sensitive to a noise, on the model accuracy also are difficult to stabilize and to converge. Furthermore, the majority of earlier works, estimate either the speed or the temperature or the winding resistance. According to the literatures, the Resilient backpropagation (RBP) as is the known as the faster BP algorithm, Cascade-Forward Neural Network (CFNN), is known as the among accelerated learning backpropagation algorithms, that's why where it is found in several researches, also in several applications in these few years. The main objective of this paper is to introduce an intelligent sensor based on resilient BP to estimate simultaneously the speed, armature temperature and resistance of brushed DC machines only from the measured current and voltage. A comparison between the obtained results and the results of traditional estimator has been made to prove the ability of the proposed method. This method can be embedded in thermal monitoring systems, in high performance motor drives.

Related papers

• SecONN: An Optical Neural Network Framework with Concurrent Detection of Thermal Fault Injection Attacks [0.7262345640500065]
  This paper first proposes a threat of thermal fault injection attacks on SPAAs based on Vector-Matrix Multipliers (VMMs) utilizing Mach-Zhender Interferometers. This paper then proposes SecONN, an optical neural network framework that is capable of not only inferences but also concurrent detection of the attacks.
  arXiv  Detail & Related papers  (2024-11-22T05:31:36Z)
• Parameter Estimation of Long Memory Stochastic Processes with Deep Neural Networks [0.0]
  We present a purely deep neural network-based approach for estimating long memory parameters of time series models. Parameters, such as the Hurst exponent, are critical in characterizing the long-range dependence, roughness, and self-similarity of processes.
  arXiv  Detail & Related papers  (2024-10-03T03:14:58Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Deep Learning Architectures for FSCV, a Comparison [0.0]
  Suitability is determined by the predictive performance in the "out-of-probe" case, the response to artificially induced electrical noise, and the ability to predict when the model will be errant for a given probe. The InceptionTime architecture, a deep convolutional neural network, has the best absolute predictive performance of the models tested but was more susceptible to noise. A naive multilayer perceptron architecture had the second lowest prediction error and was less affected by the artificial noise, suggesting that convolutions may not be as important for this task as one might suspect.
  arXiv  Detail & Related papers  (2022-12-05T00:20:10Z)
• Physics-constrained deep neural network method for estimating parameters in a redox flow battery [68.8204255655161]
  We present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium flow battery (VRFB) We show that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training.
  arXiv  Detail & Related papers  (2021-06-21T23:42:58Z)
• Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks [0.0]
  Full-waveform inversion (FWI) velocity modeling is an iterative advanced technique that provides an accurate and detailed velocity field model. In this study, we propose a method of generating velocity field models, as detailed as those obtained through FWI, using a conditional generative adversarial network (cGAN) with multiple inputs.
  arXiv  Detail & Related papers  (2021-04-30T21:41:24Z)
• A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks [0.0]
  Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem. This article presents a new approach for power disaggregation by using a deep recurrent long short term memory (LSTM) network combined with convolutional neural networks (CNN)
  arXiv  Detail & Related papers  (2021-04-15T22:34:20Z)
• Deep Networks for Direction-of-Arrival Estimation in Low SNR [89.45026632977456]
  We introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix. We train a CNN in the low-SNR regime to predict DoAs across all SNRs. Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.
  arXiv  Detail & Related papers  (2020-11-17T12:52:18Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [65.36115045035903]
  We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
  arXiv  Detail & Related papers  (2020-01-24T22:58:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.