Low-Cost MEMS-Based NARX Model for GPS-Denied Areas
01 Oct 2020-Vol. 20, Iss: 4, pp 58-70
About: The article was published on 2020-10-01 and is currently open access. It has received None citations till now.
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TL;DR: This work studies a class of recurrent network models in which the connectivity is a sum of a random part and a minimal, low-dimensional structure and shows that the dynamics are low dimensional and can be directly inferred from connectivity using a geometrical approach.
247 citations
TL;DR: In this article, a non-linear autoregressive artificial neural network (ANN) with exogenous multi-variable input (NARX) was used for short-term load forecasting.
Abstract: Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.
88 citations
TL;DR: It is proven that the second-order Bessel–Legendre inequality (BLI), which is based on an orthogonal polynomial sequence, outperforms the second to third-order integral inequality recently established based on a nonorthogonalPoC sequence.
Abstract: This paper is concerned with energy-to-peak state estimation on static neural networks (SNNs) with interval time-varying delays. The objective is to design suitable delay-dependent state estimators such that the peak value of the estimation error state can be minimized for all disturbances with bounded energy. Note that the Lyapunov–Krasovskii functional (LKF) method plus proper integral inequalities provides a powerful tool in stability analysis and state estimation of delayed NNs. The main contribution of this paper lies in three points: 1) the relationship between two integral inequalities based on orthogonal and nonorthogonal polynomial sequences is disclosed. It is proven that the second-order Bessel–Legendre inequality (BLI), which is based on an orthogonal polynomial sequence, outperforms the second-order integral inequality recently established based on a nonorthogonal polynomial sequence; 2) the LKF method together with the second-order BLI is employed to derive some novel sufficient conditions such that the resulting estimation error system is globally asymptotically stable with desirable energy-to-peak performance, in which two types of time-varying delays are considered, allowing its derivative information is partly known or totally unknown; and 3) a linear-matrix-inequality-based approach is presented to design energy-to-peak state estimators for SNNs with two types of time-varying delays, whose efficiency is demonstrated via two widely studied numerical examples.
37 citations
01 Dec 2010
TL;DR: A new system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN) based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks.
Abstract: Navigation systems used in recent days rely mainly on Kalman filter to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In common, INS/GPS data fusion provides reliable navigation solution by overcoming drawbacks such as signal blockage for GPS and increase in position errors with time for INS. Kalman filtering INS/GPS integration techniques used in present days have some inadequacies related to the stochastic error models of inertial sensors, immunity to noise, and observability. This paper aims to introduce a new system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential GPS (DGPS). Though the integrated system using multi-layer perceptron scheme improves the positioning accuracy, it has shortcomings like complexity with respect to the architecture of multi-layer perceptron networks and limitation of online training algorithm to provide real-time capabilities. This paper, therefore, proposes the use of an alternative ANN architecture. The proposed architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks. The RBF-ANN module is trained to predict the INS position error and provide accurate positioning of the moving vehicle.
27 citations
TL;DR: The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.09% compared with the INS only, WSN, EKF, and IEKF.
Abstract: As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.
19 citations