Neural Networks Applied to Gait Control of Physically Based Simulated Robots

2006 ◽  
Author(s):  
Milton Heinen ◽  
Fernando Osorio
Keyword(s):  
Neural Networks ◽  
Gait Control ◽  
Physically Based

scholarly journals Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 150 ◽  
Author(s):  
Feiyan Chen ◽  
Zhigao Zhou ◽  
Aiwen Lin ◽  
Jiqiang Niu ◽  
Wenmin Qin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Spatiotemporal Variation ◽  
Multivariate Adaptive Regression Splines ◽  
Accurate Estimation ◽  
The Tibetan Plateau ◽  
Learning Methods ◽  
Physically Based Model ◽  
Machine Learning Methods ◽  
Physically Based

Accurate estimation of direct horizontal irradiance (DHI) is a prerequisite for the design and location of concentrated solar power thermal systems. Previous studies have shown that DHI observation stations are too sparsely distributed to meet requirements, as a result of the high construction and maintenance costs of observation platforms. Satellite retrieval and reanalysis have been widely used for estimating DHI, but their accuracy needs to be further improved. In addition, numerous modelling techniques have been used for this purpose worldwide. In this study, we apply five machine learning methods: back propagation neural networks (BP), general regression neural networks (GRNN), genetic algorithm (Genetic), M5 model tree (M5Tree), multivariate adaptive regression splines (MARS); and a physically based model, Yang’s hybrid model (YHM). Daily meteorological variables, including air temperature (T), relative humidity (RH), surface pressure (SP), and sunshine duration (SD) were obtained from 839 China Meteorological Administration (CMA) stations in different climatic zones across China and were used as data inputs for the six models. DHI observations at 16 CMA radiation stations were used to validate their accuracy. The results indicate that the capability of M5Tree was superior to BP, GRNN, Genetic, MARS and YHM, with the lowest values of daily root mean square (RMSE) of 1.989 MJ m−2day−1, and the highest correlation coefficient (R = 0.956), respectively. Then, monthly and annual mean DHI during 1960–2016 were calculated to reveal the spatiotemporal variation of DHI across China, using daily meteorological data based on the M5tree model. The results indicated a significantly decreasing trend with a rate of −0.019 MJ m−2during 1960–2016, and the monthly and annual DHI values of the Tibetan Plateau are the highest, while whereas the lowest values occur in the southeastern part of the Yunnan−Guizhou Plateau, the Sichuan Basin and most of the southern Yangtze River Basin. The possible causes for spatiotemporal variation of DHI across China were investigated by discussing cloud and aerosol loading.

scholarly journals Gait Control for a Legged Robot Using Neural Networks with Dynamically-Rearranging Function

1999 ◽  
Vol 119 (12) ◽  
pp. 1567-1572
Author(s):  
Akinobu Fujii ◽  
Akio Ishiguro ◽  
Yoshiki Uchikawa ◽  
Aoki Takeshi ◽  
Peter Eggenberger
Keyword(s):  
Neural Networks ◽  
Legged Robot ◽  
Gait Control
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