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...
State estimation computation using the existing Von-Neumann type computer is reaching a limit as far as the solution techniques are concerned, and it is very difficult to expect much faster methods. In order to solve the problem, the authors employ a neural network theory, the Hopfield network ...
Various recurrent neural network architectures for solving the problems of parameter estimation in dynamical systems are presented. The architectures based... JR Raol,H Madhuranath - IEE Proceedings - Control Theory and Applications 被引量: 30发表: 1996年 H-infinite State Estimation for Takagi-Sugeno...
The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, ...
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,
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...
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...
This paper investigates the H state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so...
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...
[60] examines the effect of neural network size on state estimation accuracy. They begin by experimenting with various hidden layer sizes ranging from three to five, while maintaining a value of 32 neurons per layer. Then they set the number of hidden layers to three, the activation function ...