Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

Sun, Lei; Yi, Wei-Jian; Ding, Yuan; Guan, Jiancheng

doi:10.1155/2019/5810174

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…In order to address these issues, the past output data of the developed predictive model can be involved in the input's data vector by adopting an external time delay operator if the typical feedforward ANN technique is applied [12]. This raises the input's data vector dimension, increases computational time, and reduces convergence speed [20].…”

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

“…Compared to feedforward ANNs such as the backpropagation ANN [23], there is an additional layer named the context links layer that receives feedback signals from the hidden layer and store them as historical data. Then, these historical data are used as inputs for the hidden layer via the context links layer that is associated with a delay operator − of order TDL [20]. The context links layer boost the learning ability of ANN technique to approximate dynamic and nonlinear processes, such as solar radiation time series forecasting [21].…”

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

“…The ENN model is supposed to have inputs' features { 1 , 2 , … , } , h neurons in the hidden layer { 1 , 2 , … , ℎ } , a context links layer { 1 , 2 , … , } with dimension, and n outputs' signals { 1 , 2 , … , } [19,20,22]. The output signal of the kth context link is defined as:…”

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Jallal¹,

Yassini²,

Chabaa³

et al. 2020

RIA

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Nowadays, the studies that address solar radiation (SR) forecasting tend to focus on the implementation of conventional techniques. This provides good results, but researchers should focus on the creation of new methodologies that help us in going further and boost the prediction accuracy of SR data. The prime aim of this research study is to propose an efficient deep learning (DL) algorithm that can handle nonlinearities and dynamic behaviors of the meteorological data, and generate accurate real-time forecasting of hourly global solar radiation (GSR) data of the city of El Kelaa des Sraghna (32°2’53”N 7°24’30”W), Morocco. The proposed DL algorithm integrates the dynamic model named Elman neural network with a new input configuration-based autoregressive process in order to learn from the seasonal patterns of the historical SR measurements, and the actual measurements of air temperature. The attained performance proves the reliability and the accuracy of the proposed model to forecast the hourly GSR time series in case of missing values detection or pyranometer damage. Hence, electrical power engineers can adopt this forecasting tool to improve the integration of solar power resources into the power grid system.

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Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Jallal¹,

Yassini²,

Chabaa³

et al. 2020

RIA

View full text Add to dashboard Cite

show abstract

“…The number of cells in the hidden layer depends on the difficulty and complexity of the question. This is a Feed-Backward Neural Network [19]. each input layer output is an input to each hidden layer cell.…”

Section: Elman Neural Networkmentioning

confidence: 99%

Improving face recognition by elman neural network using curvelet transform and HSI color space

Abdullah¹,

Abed²,

Al_Barazanchi³

2019

PEN

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In this paper, a suggested algorithm was proposed to increase the efficiency of the Elman neural algorithm in face recognition. The proposed algorithm was studied on the images of 20 students from the Department of Computer Science, Tikrit University. First step creates dataset of faces, second step convert color space to HSI and using saturation layer, image decomposition using curvelet transform, feature extraction using Principle component analysis, and final step face recognition using Elman neural network. after applying proposed algorithm, the rate of face recognition 94%.

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“…Combined with fuzzy inference system (FIS), the initial alignment schemes based on fuzzy adaptive filters, such as the fuzzy adaptive Kalman filter [ 37 ], fuzzy simplified UKF [ 38 ] and fuzzy strong tracking UKF [ 39 , 40 ], etc., were proposed to improve the performance of SINS initial alignment under large alignment angles and disturbance. In [ 41 , 42 , 43 , 44 , 45 ], the SINS initial alignment methods utilizing neural networks were researched, which could be applied for the application scenarios with disturbances, for example, Shipborne, guided projectiles, and so on. In [ 46 , 47 ], two machine learning methods were proposed to achieve the nonlinear initial alignment of SINS under the condition of large misalignment angles, of which one was based on Gaussian process regression (GPR) [ 46 ], the other utilized a combination of Gaussian mixture model (GMM), expectation–maximization (EM), and UKF filter [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Zhao

Yan

Yong-yuan

et al. 2020

Sensors

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In order to achieve the fine alignment of strapdown inertial navigation (SINS) under large misalignment angles, a novel filtering alignment method is proposed based on the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS). Firstly, the quaternion is employed to represent the attitude errors of SINS. A second-order nonlinear state equation is made based on the nonlinear velocity error model and attitude error model, and the linear measurement equation is based on the velocity outputs from SINS. Then, the filtering scheme is designed based on EKF2 and AFIS. The error estimation and fine alignment can be achieved by using the proposed filtering scheme. The results of Monte Carlo Simulation show that the errors of pitch, roll and yaw misalignment angles quickly decrease to about 14″, 15″ and 7.62′ respectively in 350 s under the condition of any misalignment angles with pitch error from −40° to 40°, roll error from −40° to 40°, and yaw error from −50° to 50°. Even when the initial misalignment angles are all very large such as (80°, 120°, 170°), the proposed nonlinear alignment method still can converge normally by utilizing the adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k−1. Finally, the turntable experiment was performed, and the effectiveness and superiority of the proposed method were further verified by compared with other nonlinear methods.

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Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

Cited by 7 publications

References 12 publications

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Improving face recognition by elman neural network using curvelet transform and HSI color space

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

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