Radial basis function neural network algorithm for semi-active control of base-isolated structures

2017 ◽  
Vol 24 (10) ◽  
pp. e1984 ◽  
Author(s):  
Agrahara Krishnamoorthy ◽  
Shubha Bhat ◽  
Dattatreya Bhasari
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Active Control ◽  
Basis Function ◽  
Network Algorithm ◽  
Radial Basis ◽  
Neural Network Algorithm ◽  
Isolated Structures

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.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document