Application of Backpropagation Neural Networks in Calculation of Robot Kinematics

2014 ◽  
pp. 379-390
Author(s):  
R Srikant ◽  
Ch Rao
Keyword(s):  
Neural Networks ◽  
Robot Kinematics ◽  
Backpropagation Neural Networks

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Control Method ◽  
Joint Space ◽  
Robot Kinematics ◽  
Rbf Neural Networks ◽  
Task Space ◽  
Actuator Dynamics ◽  
Rigid Link ◽  
On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

PREDIKSI PERTUMBUHAN PENDUDUK PADA PROVINSI SUMATERA BARAT DENGAN MENGGUNAKAN METODE BACKPROPAGATION

2020 ◽  
Vol 7 (4) ◽  
pp. 216
Author(s):  
Hijrah Yanti Sitanggang ◽  
Vera Irma Delianti
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Population Growth ◽  
Population Growth Rate ◽  
Backpropagation Neural Networks ◽  
West Sumatra ◽  
Artificial Neural ◽  
The City

The problem of population is one of the problems in the Province of West Sumatra, especially in the City of Padang, Kota Bukitinggi, and the City of Payakumbuh which has a very fast population growth rate, this occurs due to several factors such as births, deaths, residents who come, and residents who leave. The highest population growth occurred in Padang City in 2018 amounting to 939,112 residents and the smallest population growth occurred in the City of Bukitinggi in 2014 amounting to 120,491 residents. The purpose of this study is to predict population growth that will occur in 2019 in the cities of Padang, Bukittinggi and Payakumbuh. The method used in this research is descriptive correlational by applying backpropagation neural networks. The application used is Matlab. Based on the problems and methods obtained, the predicted results in 2019 in Padang City amounted to 124,7150, Bukittinggi numbered 126,8040 and Payakumbuh totaled 128.7830.  Keywords: Artificial Neural Networks, Backpropagation, Matlab.

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