Source discrimination of mine water inrush based on Elman neural network globally optimized by genetic algorithm

The estimation of continuous and simultaneous multijoint angle based on surface electromyography (sEMG) signal is of considerable significance in rehabilitation practice. However, there are few studies on the continuous joint angle of multiple joints at present. In this paper, the wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal. An Elman neural network optimized by genetic algorithm (GA) was established to estimate the joint angle of shoulder and elbow. First, the accuracy of the method is verified by estimating the angle of the shoulder joint. Then, this method was used to simultaneously and continuously estimate the shoulder and elbow joint angle. Six subjects flexed and extended the upper limbs according to the intended movements of the experiment. The results show that this method can obtain a decent performance with a RMSE of 3.4717 and R2 of 0.8283 in shoulder movement and with a RMSE of 4.1582 and R2 of 0.8114 in continuous synchronous movement of the shoulder and elbow.

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

Mathematical Problems in Engineering ◽

10.1155/2019/5810174 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Lei Sun ◽

Wenjun Yi ◽

Dandan Yuan ◽

Jun Guan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Navigation System ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Strapdown Inertial Navigation System ◽

Initial Alignment ◽

Elman Neural Network ◽

Network Algorithm ◽

Neural Network Algorithm

The purpose of this paper is to present an in-flight initial alignment method for the guided projectiles, obtained after launching, and utilizing the characteristic of the inertial device of a strapdown inertial navigation system. This method uses an Elman neural network algorithm, optimized by genetic algorithm in the initial alignment calculation. The algorithm is discussed in details and applied to the initial alignment process of the proposed guided projectile. Simulation results show the advantages of the optimized Elman neural network algorithm for the initial alignment problem of the strapdown inertial navigation system. It can not only obtain the same high-precision alignment as the traditional Kalman filter but also improve the real-time performance of the system.

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Online Elman Neural Network Training by Genetic Algorithm

British Journal of Mathematics & Computer Science ◽

10.9734/bjmcs/2016/29060 ◽

2016 ◽

Vol 19 (1) ◽

pp. 1-15

Author(s):

Ali Hasan ◽

Watheq Laith

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Network Training ◽

Elman Neural Network ◽

Network Training

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Short-Term Load Forecasting Based on GA-Elman Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.986-987.520 ◽

2014 ◽

Vol 986-987 ◽

pp. 520-523

Author(s):

Wen Xia You ◽

Jun Xiao Chang ◽

Zi Heng Zhou ◽

Ji Lu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Relative Error ◽

Bp Neural Network ◽

Load Forecasting ◽

Short Term ◽

Elman Neural Network ◽

Learning Efficiency ◽

Multiple Layer ◽

Short Term Load Forecasting

Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

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