A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach

2012 ◽  
Vol 193 ◽  
pp. 62-84 ◽  
Author(s):  
Antonios D. Niros ◽  
George E. Tsekouras
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Rbf Neural Network ◽  
Training Algorithm ◽  
Clustering Approach

A New Training Algorithm for RBF Neural Network based on Dynamic Fuzzy Clustering

2012 ◽  
Vol 241-244 ◽  
pp. 1593-1597
Author(s):  
Yan Jun Cui ◽  
Yan Dong Ma ◽  
Jie Li ◽  
Zheng Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Traditional Method ◽  
Rbf Neural Network ◽  
Training Algorithm ◽  
Clustering Method ◽  
Fuzzy Clustering Method ◽  
Complex Nonlinear Systems ◽  
Hidden Nodes ◽  
Dynamic Fuzzy Clustering

A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper. This algorithm is based on the dynamic fuzzy clustering method (DFCM). The algorithm has a number of advantages compared to the traditional method based on k-means. For example, it does not need to know the number of the hidden nodes and to predicts more accurately. Due to these advantages, this method proves to be suitable for developing models for complex nonlinear systems.

Research on Nonlinear Approximation Model of Radial Basis Function Neural Network Trained Using Artificial Fish Swarm Algorithm with Adaptive Adjustment

2015 ◽  
Vol 713-715 ◽  
pp. 1855-1858 ◽  
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Nonlinear Approximation ◽  
Nonlinear Function ◽  
Training Algorithm ◽  
Step Size ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Adaptive Adjustment ◽  
Swarm Algorithm

In order to improve the modeling efficiency of RBF neural network, an Artificial Fish Swarm Algorithm (AFSA) training algorithm with an adaptive mechanism is proposed. In the training algorithm, the search step size and visible domain of AFSA algorithm can be adjusted dynamically according to the convergence characteristics of artificial fish swarm, and then the improved AFSA algorithm is used to optimize the parameters of RBF neural network. The example shows that, the proposed model is a better approximation performance for the nonlinear function.

Research on Quality Evaluation of Online Reservation Hotel APP Based on a RBF Neural Network and Support Vector Machine

2020 ◽  
Vol 12 (2) ◽  
pp. 50-64
Author(s):  
Ma Xiang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Rbf Neural Network ◽  
Evaluation Index System ◽  
Support Vector ◽  
Training Algorithm ◽  
Basic Model ◽  
Expert Interview ◽  
Online Reservation

In order to evaluate the quality of online reservation hotel APP, RBF neural and support vector machine are used to evaluate the quality of online reservation hotel APP. First, the basic theory of the RBF neural network is studied, and the training algorithm of the RBF neural network is designed. Second, the basic model of support vector machine is analyzed, and the training algorithm is designed. Third, the evaluation index system of online reservation hotel APP is designed, and the weight of every index is established based on questionnaires and expert interview, and the evaluation simulation is carried out for 25 online reservation hotel APP, results show that the RBF neural network and support vector machine can obtain consistent evaluation results, and the support vector machine has better evaluation performance.

scholarly journals Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network

2020 ◽  
Vol 213 ◽  
pp. 03002
Author(s):  
Guozhen Ma ◽  
Po Hu ◽  
Yunjia Wang ◽  
Yongli Wang ◽  
Chengcong Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Rbf Neural Network ◽  
Distribution Network ◽  
Load Forecasting ◽  
Current Load ◽  
Short Term ◽  
Forecast Period ◽  
Short Term Load Forecasting ◽  
Load Characteristics

In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document