Modeling bimanual coordination using back propagation neural network and radial basis function network

2014 ◽  
Author(s):  
Tomomi Mahira ◽  
Nevrez Imamoglu ◽  
Jose Gomez-Tames ◽  
Kahori Kita ◽  
Wenwei Yu
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Bimanual Coordination ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Radial Basis ◽  
Function Network

Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor

2019 ◽  
Vol 19 (1) ◽  
pp. 281-292
Author(s):  
Junkyeong Kim ◽  
Seunghee Park
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tensile Force ◽  
Radial Basis Function Network ◽  
Feedforward Neural Network ◽  
Concrete Bridges ◽  
Force Estimation ◽  
Radial Basis ◽  
Function Network

It has been proposed that pre-stressed concrete bridges improve load performance by inducing axial pre-stress using pre-stress tendons. However, the tensile force of the pre-stress tendons could not be managed after construction, although it directly supports the load of the structure. Thus, the tensile force of the pre-stress tendon should be checked for structural health monitoring of pre-stressed concrete bridges. In this study, a machine learning–based tensile force estimation method for a pre-stressed concrete girder is proposed using an embedded elasto-magnetic sensor and machine learning method. The feedforward neural network and radial basis function network were applied to estimate the tensile force of the pre-stress tendon using the area ratio of the magnetic hysteresis curve measured by the embedded elasto-magnetic sensor. The feedforward neural network and radial basis function network were trained using 213 datasets obtained in laboratory experiments, and trained feedforward neural network and radial basis function network were applied to a 50-m real-scale pre-stressed concrete girder test for estimating tensile force. Nine embedded elasto-magnetic sensors were installed on the sheath, and the magnetic hysteresis curves of the pre-stress tendons were measured during tensioning. The area ratio was extracted and inputted to the trained feedforward neural network and radial basis function network to estimate the tensile force. The estimated tensile force was compared with the reference tensile force measured by the load cell. According to the result, the estimated tensile force can represent the actual tensile force of the pre-stress tendon without calibrating tensile force estimation algorithms at the site. In addition, it can measure the actual friction loss by estimating the tensile force at the maximum eccentric part. Based on the results, the proposed method might be a solution for the structural health monitoring of pre-stressed concrete bridges with field applicability.

Application of Radial Basis Function Neural Network on the Prediction of Urban Built-Up Area

2014 ◽  
Vol 556-562 ◽  
pp. 5308-5311
Author(s):  
Li Hua Chen ◽  
Yu Chen Wang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Quantitative Prediction ◽  
Basic Issue ◽  
Radial Basis ◽  
Neural Network Prediction ◽  
Network Prediction

The study on the prediction of urban built-up area is the basic issue in urban planning. This paper takes the prediction of urban built-up area of Hefei city as an example, building a factor system that affects built-up area from the economic, social and environmental dimensions. Then, the paper establishes a quantitative prediction model based on the Radial Basis Function neural network. As a comparison, the paper also uses the Back Propagation neural network to predict. The results show that the Radial Basis Function neural network prediction has a higher accuracy and the prediction result is more reasonable and reliable.

Vibration Fault Diagnosis of Rotating Machinery Using PSO-Based Radial Basis Function Network

2013 ◽  
Vol 302 ◽  
pp. 474-480
Author(s):  
Huo Ching Sun ◽  
Chao Ming Huang ◽  
Yann Chang Huang ◽  
Hsing Feng Chen
Keyword(s):  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Pso Algorithm ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Radial Basis ◽  
Function Network

A particle swarm optimization-based radial basis function network (PSO-RBFN) is presented to diagnose vibration faults of steam turbine-generator sets (STGS) in a power plant. The proposed PSO algorithm is used to automatically tune the control parameters of the RBFN. The test results demonstrate that the proposed PSO-RBFN has a higher diagnostic accuracy than the RBFN and multilayer perceptron network (MLPN) trained by error back-propagation algorithm. Moreover, this paper has demonstrated that the proposed PSO-RBFN can be as a reliable tool for vibration fault diagnosis of STGS.

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