An approach to control or monitoring of battery operation makes use of a recurrent neural network (RNN), which receives one or more battery attributes for a Lithium ion (Li-ion) battery, and determines, based on the received one or more battery attributes, a state-of-charge (SOC) estimate...
NARX neural network (based on the nonlinear autoregressive with exogenous inputs neural network) is a nonlinear dynamic neural network, which can learn and predict the next time series according to the previous value (feedback) of the same time series and another time series (external time series...
Recently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However,
In the past few years, many important results have been available on various analysis aspects for state estimation of neural networks, see [13], [14], [15] for some recent publications. It is well known that time delays are common features in the implementation of a neural network due to...
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a “mean value” model, and anticipate maintenance requirements. The PINN model is applied to diesel engines...
Volumetric Regression Network(VRN) 本文作者使用的模型,由多个沙漏模型组合在一起形成。 VRN模型使用两个沙漏模块堆积而成,并且没有使用hourglass的间接监督结构。 VRN-guided 模型是使用了Stacked Hourglass Networks for Human Pose Estimation 的工作作为基础,在前半部分使用两个沙漏模块用来获取68个标记点,后半部分...
This study proposes an end-to-end prognostic framework for state-of-health (SOH) estimation and remaining useful life (RUL) prediction. In such a framework, a hybrid neural network (NN), i.e., the concatenation of one-dimensional convolutional NN and active-state-tracking long–short-term me...
During training, you can stop training and return the current state of the network by clicking the stop button in the top-right corner. After you click the stop button, it can take a while for training to complete. Once training is complete, trainnet returns the trained network. Specify th...
Associative memory networks: A type of recurrent network whose equilibrium state is used to memorize information. Self-organizing networks: The neurons are organized on a sort of dynamic map that evolves during the learning process, in a way that is sensitive to the history and neighboring neuron...
Data-driven modeling techniques for state estimation 5.4.4.1 ANN training procedure The procedure to train a typical backpropagation network is described. The problem of neural network training is to obtain a set of weights such that the prediction error defined by the difference between the network...