Prediction Error of a Fault Tolerant Neural Network

2006 ◽  
pp. 521-528 ◽  
Author(s):  
John Sum ◽  
Chi-sing Leung ◽  
Kevin Ho
Keyword(s):  
Neural Network ◽  
Prediction Error ◽  
Fault Tolerant

Prediction error of a fault tolerant neural network

2008 ◽  
Vol 72 (1-3) ◽  
pp. 653-658 ◽  
Author(s):  
John Sum ◽  
Andrew Chi-Sing Leung
Keyword(s):  
Neural Network ◽  
Prediction Error ◽  
Fault Tolerant

Application of BP Neural Network into the Kow of Chemical Contaminants

2010 ◽  
Vol 121-122 ◽  
pp. 574-578
Author(s):  
Hui Yu Jiang ◽  
Min Dong ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Partition Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Physical Parameters ◽  
Bp Network ◽  
Chemical Contaminants ◽  
Water Partition Coefficient ◽  
Molecular Bond

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 740-746
Author(s):  
Hang Yuan ◽  
Chen Lu ◽  
Ze Tao Xiong ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Simulation Model ◽  
Working Conditions ◽  
Detection Method ◽  
Fault Tolerant ◽  
Adaptive Threshold ◽  
Residual Error ◽  
The Other ◽  
Aerodynamic Loads

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

2018 ◽  
Vol 233 (10) ◽  
pp. 2467-2478 ◽  
Author(s):  
Radhika Raveendran ◽  
Apoorva Suresh ◽  
Vignesh Rajaram ◽  
Shankar C Subramanian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Confidence Interval ◽  
Prediction Error ◽  
Performance Degradation ◽  
Dynamics Simulation ◽  
Brake System ◽  
Road Vehicles ◽  
Prediction Scheme ◽  
Artificial Neural

In heavy commercial road vehicles, the air brake system is a critical vehicle safety system whose performance degradation increases the risk of accidents and hence requires periodic inspection and maintenance. The wear of brake pad lining and brake drum during operation leads to increase in the stroke of a component called pushrod whose ‘out-of-adjustment’ creates severe brake performance degradation. The fact that the driver does not receive a corresponding tactile feedback till it is too severe adds to the complexity of manual detection. Motivated by the increase in onboard sensing, electronics, and computation capabilities, this study proposes an artificial neural network–based approach to predict pushrod stroke based on measurement of brake chamber pressure. Here, a back propagation algorithm was used to train the multilayer feed-forward network. The effect of excessive pushrod stroke on vehicle braking response was first studied using a Hardware-in-Loop system that consists of brake system hardware and a commercial vehicle dynamics simulation software (IPG TruckMaker®). Experimental data collected from this system with manual slack adjuster and automatic slack adjuster have then been used to train and test the artificial neural network for pushrod stroke prediction. The performance of the prediction scheme has been tested over the entire range of brake operating conditions. The prediction error corresponding to manual slack adjuster was found to be within ±15% in 322 out of the entire test set of 328 instances (98.17%) and automatic slack adjuster within ±8% in all 57 test sets (100%). Statistical analysis based on confidence interval revealed a prediction error between −1.62% and −3.05% for manual slack adjuster and 0.43% and −1.62% for automatic slack adjuster for 99% confidence interval, which demonstrated the efficacy of the proposed prediction scheme.

scholarly journals Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chaohui Wang ◽  
Songyuan Tan ◽  
Qian Chen ◽  
Jiguo Han ◽  
Liang Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Multiple Regression ◽  
Dynamic Modulus ◽  
Prediction Error ◽  
Prediction Models ◽  
Asphalt Mixture ◽  
High Modulus ◽  
Output Stability ◽  
Svm Model

Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture.

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