scholarly journals Vehicle Sideslip Angle Estimation Based on General Regression Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wang Wei ◽  
Bei Shaoyi ◽  
Zhang Lanchun ◽  
Zhu Kai ◽  
Wang Yongzhi ◽  
...  
Keyword(s):  
Neural Network ◽  
Estimation Method ◽  
Stability Control ◽  
Lateral Acceleration ◽  
General Regression Neural Network ◽  
Slip Angle ◽  
Sideslip Angle ◽  
Training Samples ◽  
Stability Control System ◽  
General Regression

Aiming at the accuracy of estimation of vehicle’s mass center sideslip angle, an estimation method of slip angle based on general regression neural network (GRNN) and driver-vehicle closed-loop system has been proposed: regarding vehicle’s sideslip angle as time series mapping of yaw speed and lateral acceleration; using homogeneous design project to optimize the training samples; building the mapping relationship among sideslip angle, yaw speed, and lateral acceleration; at the same time, using experimental method to measure vehicle’s sideslip angle to verify validity of this method. Estimation results of neural network and real vehicle experiment show the same changing tendency. The mean of error is within 10% of test result’s amplitude. Results show GRNN can estimate vehicle’s sideslip angle correctly. It can offer a reference to the application of vehicle’s stability control system on vehicle’s state estimation.

Vehicle Sideslip Angle Estimation Through Neural Networks: Application to Experimental Data

2006 ◽  
Author(s):  
Stefano Melzi ◽  
Edoardo Sabbioni ◽  
Alessandro Concas ◽  
Marco Pesce
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Lateral Acceleration ◽  
Slip Angle ◽  
Vehicle Speed ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Training Stage ◽  
Wide Range ◽  
Self Adaptation

This work explores the possibility of using a non-structured algorithm as a sideslip angle valuer: on the basis of a preliminary numerical analysis, a neural network was designed and trained with experimental signals of lateral acceleration, vehicle speed, yaw rate and steer angle. The network was applied to experimental data in order to verify its capability of self-adaptation to changes in friction coefficient and to provide accurate estimations for manoeuvres sensibly different from the ones used during the training stage. The simple architecture joined with an appropriate training set conferred good self-adaptation properties to the neural network which was able to provide satisfying estimation of side slip angle for a wide range of manoeuvres and different friction conditions.

Study of Vehicle Sideslip Angle Real-Time Estimation Method

2013 ◽  
Vol 846-847 ◽  
pp. 26-29
Author(s):  
Xiao Bin Fan ◽  
Pan Deng
Keyword(s):  
Real Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Stability Control ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Slip Angle ◽  
Sideslip Angle ◽  
Direct Integration Method ◽  
Real Time Estimation

In the vehicle stability control and other active safety systems, vehicle sideslip angle real-time estimation is necessary. However, the direct measurement of sideslip angle is more difficult or too costly, so it is often used in estimating methods. The vehicle sideslip angle of closed-loop Luenberger observer and Kalman observer were constructed based on two degrees of freedom bicycle model, as well as the direct integration method for large sideslip angle conditions. The comparative study showed that Kalman filtering estimation method and Luenberger estimation methods have better estimation accuracy in small slip angle range.

Application of General Regression Neural Network in Characteristic Curve Fitting of Optical Fiber Micro-Bend Sensor

2013 ◽  
Vol 441 ◽  
pp. 116-119 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Optical Fiber ◽  
Characteristic Curve ◽  
New Method ◽  
General Regression Neural Network ◽  
Small Scale ◽  
Training Samples ◽  
Nonlinear Compensation ◽  
General Regression ◽  
Bend Sensor

In order to reflect the input and output features of an optical fiber micro-bend sensor, a new method using general regression neural network (GRNN) to fit the characteristic curve is proposed in this paper. First, the measuring principle of optical fiber micro-bend sensor and the principle of GRNN are introduced. Then, to verify the feasibility and effectiveness of this new method, a comparison between two kinds of fitting methods is done. One is based on GRNN, the other is based on Levenberg-Marquart improved BPNN. The results of the simulation experiment show that with the same number of training samples and for small scale to medium scale networks, compared with BPNN, GRNN has smaller error, faster convergence speed and higher fitting accuracy. So the method discussed in this paper provides a reliable basis for the nonlinear compensation problem of optical fiber micro-bend sensor.

Simulating Research of Integrated Control for Automobile Steering Stability

2011 ◽  
Vol 130-134 ◽  
pp. 2190-2193
Author(s):  
Chuan Long Shi ◽  
Chuan Hui Liu
Keyword(s):  
Integrated Control ◽  
Nonlinear Effects ◽  
Stability Control ◽  
Lateral Acceleration ◽  
Slip Angle ◽  
Software Environment ◽  
Stability Performance ◽  
Stability Control System ◽  
Integrated Controller ◽  
Yaw Moment

In this paper, four-wheel steering and direct yaw-moment integrated controller is designed. To verify the effectiveness of the integrated controller, a nonlinear three-degree-of-freedom model is employed for computer simulation. Considering the nonlinear effects of tyre, Pacejka tyre model was adopted to set up the nonlinear vehicle dynamic model. The direct yaw-moment controller was designed based on optimal control theory. Simulation on the nonlinear vehicle with integrated controller in Matlab/Simulink software environment was described. The simulations suggest, compared with FWS and 4WS, the integrated controller can make the handling and stability performance on big lateral acceleration and slip angle improved, and make the driver drive the vehicle normally. The conclusion can be useful for the system design of vehicle stability control system.

The Training Method of General Regression Neural Network for GDOP Approximation

2013 ◽  
Vol 278-280 ◽  
pp. 1265-1270
Author(s):  
Xian Li ◽  
Mei Ping Wu ◽  
Xiao Feng He ◽  
Kai Dong Zhang
Keyword(s):  
Neural Network ◽  
New Method ◽  
General Regression Neural Network ◽  
Computational Error ◽  
Approximation Accuracy ◽  
The Neural Network ◽  
Training Samples ◽  
Samples Selection ◽  
Temporal And Spatial ◽  
General Regression

Aimed at the problem of real-time and accurate Geometry Dilution of Precision (GDOP) approximation, a new method using general regression neural network (GRNN) was proposed firstly, and the training samples selection and normalization method was studied by using spectrum analysis. The computation results show that the symmetrical constellation needs 24 hours continuous samples while the hybrid one needs 72 hours to train the neural network sufficiently. Finally, the performance analysis shows that this new method has excellent performance on temporal and spatial generalization approximation accuracy, when trained GRNN are used, the GDOP computational error is less than 0.25 within 30 days, and the error is less than 0.3 within 10 degrees latitude/longitude area.

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mina Salehi ◽  
Siamak Farhadi ◽  
Ahmad Moieni ◽  
Naser Safaie ◽  
Mohsen Hesami
Keyword(s):  
Neural Network ◽  
Fruit Fly ◽  
Corylus Avellana ◽  
General Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Harvesting Time ◽  
Fruit Fly Optimization ◽  
General Regression ◽  
Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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