An Improved Fault-Tolerant Objective Function and Learning Algorithm for Training the Radial Basis Function Neural Network

In order to improve the accuracy of shooting in basketball. A shooting accuracy prediction method based on the convergent improved resource allocating network (CIRAN) online radial basis function neural network (RBFNN) is proposed, and the RBFNN learning algorithm is improved. Through the collection of shooting motion images, feature point extraction, and edge contour feature extraction, the shooting motion trajectory is obtained. Using the online neural network based on the CIRAN learning algorithm to predict the accuracy of shooting, this method analyzes the radial basis function (RBF) network. Based on the RBF analysis, the number of network layers and the number of hidden layer neurons are adjusted and optimized. In order to improve the prediction accuracy of shooting in basketball, a method based on. Through the analysis, it can be known that the accuracy of both the traditional RBFNN and the CIRAN-based online neural network for the prediction of shooting accuracy is above 70%. The prediction accuracy of the online neural network for shooting is higher than that of the traditional one. This is mainly because the online update function of the learning algorithm can better adjust the corresponding structure with the development of the game and has a better generalization ability. In addition, because the CIRAN learning algorithm introduces the hidden layer neuron deletion strategy, its network structure is simpler than that of the traditional one, the number of hidden layer neurons is less, and the running time required is less, which can better meet the real-time requirements and provide a more scientific method for basketball training.

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Fault-Tolerant Active Disturbance Rejection Control of Plant Protection of Unmanned Aerial Vehicles Based on a Spatio-Temporal RBF Neural Network

Applied Sciences ◽

10.3390/app11094084 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4084

Author(s):

Lianghao Hua ◽

Jianfeng Zhang ◽

Dejie Li ◽

Xiaobo Xi

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Flight Control ◽

Basis Function ◽

Disturbance Rejection ◽

Control Method ◽

Fault Tolerant ◽

Active Disturbance Rejection ◽

Radial Basis ◽

Spatio Temporal

This paper presents a fault-tolerant flight control method for a multi-rotor UAV under actuator failure and external wind disturbances. The control method is based on an active disturbance rejection controller (ADRC) and spatio-temporal radial basis function neural networks, which can be used to achieve the stable control of the system when the parameters of the UAV mathematical model change. Firstly, an active disturbance rejection controller with an optimized parameter design is designed for rthe obust control of a multi-rotor vehicle. Secondly, a spatio-temporal radial basis function neural network with a new adaptive kernel is designed. In addition, the output of the novel radial basis function neural network is used to estimate fusion parameters containing actuator faults and model uncertainties and, consequently, to design an active fault-tolerant controller for a multi-rotor vehicle. Finally, fault injection experiments are carried out with the Qball-X4 quadrotor UAV as a specific research object, and the experimental results show the effectiveness of the proposed self-tolerant, fault-tolerant control method.

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Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network

Sensors ◽

10.3390/s21113651 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3651

Author(s):

Qin Yang ◽

Zhaofa Ye ◽

Xuzheng Li ◽

Daozhu Wei ◽

Shunhua Chen ◽

...

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Network Structure ◽

Information Entropy ◽

Basis Function ◽

Learning Algorithm ◽

Rbf Neural Network ◽

Output Layer ◽

Radial Basis ◽

Hidden Layer

Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.

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Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm

IEEE Transactions on Neural Networks ◽

10.1109/72.661125 ◽

1998 ◽

Vol 9 (2) ◽

pp. 308-318 ◽

Cited By ~ 321

Author(s):

Lu Yingwei ◽

N. Sundararajan ◽

P. Saratchandran

Keyword(s):

Neural Network ◽

Performance Evaluation ◽

Radial Basis Function ◽

Basis Function ◽

Learning Algorithm ◽

Rbf Neural Network ◽

Neural Network Learning ◽

Network Learning ◽

Radial Basis

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Research on Learning Algorithm of RBF Neural Network Based on Extended Kalman Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.2705 ◽

2014 ◽

Vol 989-994 ◽

pp. 2705-2708

Author(s):

Xu Sheng Gan ◽

Hai Long Gao

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Radial Basis Function ◽

Extended Kalman Filter ◽

Basis Function ◽

Learning Algorithm ◽

Rbf Neural Network ◽

Learning Capability ◽

Radial Basis ◽

Neural Network Algorithm

To improve the learning capability of Radial Basis Function (RBF) neural network, a RBF neural network algorithm based on Extended Kalman Filter (EKF) is proposed. First the basic idea of EKF algorithm and RBF neural network are introduced, and then EKF is used to optimize the parameters combination of RBF neural network to obtain the better model. The experiment proves its feasibility.

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