scholarly journals Application Analysis of Radial Basis Function Neural Network Algorithm of Genetic Algorithm for Environmental Restoration and Treatment Effect Evaluation of Decommissioned Uranium Tailings Ponds

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kun Wei ◽  
Guokai Xiong ◽  
Zhenghua Xu ◽  
Yong Liu
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Environmental Stability ◽  
Stabilization Process ◽  
Radial Basis ◽  
Neural Network Algorithm ◽  
The Stability ◽  
Uranium Tailings ◽  
Tailings Ponds

A new analysis method for the environmental stability of uranium tailing ponds is established in this paper, and the stability intervals and environmental stability rates of indicators are defined in precise mathematical language and analyzed with examples. The results show that the overall environmental stability of this uranium tailings pond is still in a poor state after the first phase of decommissioning treatment, and special decommissioning treatment should be carried out for factors such as pH and radionuclides Po and Pb. Using the powerful nonlinear mapping function of the artificial neural network, a radial basis function neural network algorithm was constructed to predict the environmental stability of the uranium tailing pond. It provides a new feasible method for the comprehensive evaluation technology of uranium tailings ponds. Accuracy in DOA Estimation. The research work in this paper mainly analyzed the environmental stabilization process and stability of decommissioned uranium tailings ponds, proposed a new concept of environmental stability with ecological and environmental protection concepts and gave it a new connotation, established an environmental stability evaluation index system for decommissioned uranium tailings ponds through index screening by using rough set theory, comprehensively considered the influence of environmental factors such as external wastewater and exhaust gas, and realized the multifactor. The system of evaluation indexes for the stability of decommissioned uranium tailings ponds was established by combining multiple factors, and the long-term monitoring and modeling of the environmental stabilization process of decommissioned uranium tailings ponds was carried out by using mathematical methods. The results show that the RBFNN-GA algorithm can reduce the training error of the random radial basis function neural network, improve the generalization ability of the network, and make it capable of handling large data sets.

scholarly journals Aerodynamic Parameters Estimation Using Radial Basis Function Neural Partial Differentiation Method

2018 ◽  
Vol 68 (3) ◽  
pp. 241 ◽  
Author(s):  
Jitu Sanwale ◽  
Dhan Jeet Singh
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Flight Data ◽  
Stability And Control ◽  
Partial Differentiation ◽  
Radial Basis ◽  
Control Derivatives ◽  
The Stability ◽  
And Control

Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation of stability and control derivatives from recorded flight data. This problem is extensively studied in the past using classical approaches such as output error, filter error and equation error methods. An alternative approach to these model based methods is the machine learning such as artificial neural network. In this paper, radial basis function neural network (RBF NN) is used to model the lateral-directional force and moment coefficients. The RBF NN is trained using k-means clustering algorithm for finding the centers of radial basis function and extended Kalman filter for obtaining the weights in the output layer. Then, a new method is proposed to obtain the stability and control derivatives. The first order partial differentiation is performed analytically on the radial basis function neural network approximated output. The stability and control derivatives are computed at each training data point, thus reducing the post training time and computational efforts compared to hitherto delta method and its variants. The efficacy of the identified model and proposed neural derivative method is demonstrated using real time flight data of ATTAS aircraft. The results from the proposed approach compare well with those from the other.

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

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.

Neural tracking control of surface vehicles with lumped uncertainties

2020 ◽  
pp. 095965182097355
Author(s):  
Ying Zheng
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Trajectory Tracking ◽  
Basis Function ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Time Control ◽  
Neural Network Controller ◽  
Radial Basis ◽  
The Stability

In this article, an adaptive radial basis function neural network scheme for trajectory tracking control of surface vehicles is proposed. Under complex uncertainties, the proposed controller is designed by combining radial basis function neural network and finite-time control algorithm. Using the novel controller, the stability of accurate trajectory tracking can be ensured and the robustness of control system can be improved. Theoretical proof is proposed by Lyapunov function that the radial basis function neural network controller can make surface vehicle to accurately track desire trajectory steadily. Simulation studies conducted on a prototype CyberShip II demonstrate remarkable performance of proposed control scheme.

scholarly journals Landscape Planning and Image Analysis Based on Multipopulation Coevolution Particle Swarm Radial Basis Function Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Wang
Keyword(s):  
Neural Network ◽  
Urban Planning ◽  
Radial Basis Function ◽  
Basis Function ◽  
Landscape Planning ◽  
Particle Swarm ◽  
Planning And Design ◽  
Network Algorithm ◽  
Radial Basis ◽  
Neural Network Algorithm

Urban landscape planning and design is not only closely related to people’s living environment, but also has an important impact on urban planning and development. However, there are some problems in landscape planning and design, such as excellent cases, low reuse rate of data, discrepancy between design scheme and actual situation, and serious shortage of relevant professionals. The artificial neural network can give corresponding ways to improve and solve these problems. Therefore, this paper proposes a research on garden planning and design based on multipopulation coevolution particle swarm radial basis function neural network algorithm. Based on multipopulation coevolution particle swarm radial basis function neural network algorithm, the error between the predicted evaluation value and the actual evaluation value in the simulation experiment is less than 5%, which shows good accuracy and generalization ability in performance. And in the plant configuration simulation experiment, it can effectively evaluate the urban planning and design and put forward the corresponding adjustment scheme according to the analysis results, which is more in line with the actual needs of urban planning.

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