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.

scholarly journals Introducing a software for innovative neuro-fuzzy clustering method named NFCMR

2018 ◽  
Vol 8 (2) ◽  
pp. 62-69 ◽  
Author(s):  
Aref Shirazi ◽  
Adel Shirazy ◽  
Shahab Saki ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Mineral Exploration ◽  
General Regression Neural Network ◽  
Clustering Method ◽  
Neuro Fuzzy ◽  
Fuzzy Clustering Method ◽  
Fuzzy C Means Clustering ◽  
General Regression

An innovative neural-fuzzy clustering method is for predicting cluster (anomaly / background) of each new sample with the probability of its presence. This method which is a combination of the Fuzzy C-Means clustering method (FCM) and the General Regression Neural Network (GRNN), is an attempt to first divide the samples in the region by fuzzy method with the probability of being in each cluster and then with the results of this Practice, the artificial neural network is trained, and can analyze the new data entered in the region with the probable percentage of the clusters. More clearly, after a full mineral exploration, the sample can be attributed to a certain probable percentage of anomalies. To test the accuracy of this clustering in the form of the theory alone, a case study was conducted on the results of the analysis of regional alluvial sediments data in Birjand, IRAN, which resulted in satisfactory results. This software is written in MATLAB and its first application in mining engineering. This algorithm can be used in other similar applications in various sciences.

scholarly journals Design of Polynomial Fuzzy Radial Basis Function Neural Networks Based on Nonsymmetric Fuzzy Clustering and Parallel Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Wei Huang ◽  
Jinsong Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Fuzzy Clustering ◽  
Basis Function ◽  
Parallel Optimization ◽  
Clustering Method ◽  
Fair Competition ◽  
Fuzzy Clustering Method ◽  
Radial Basis

We first propose a Parallel Space Search Algorithm (PSSA) and then introduce a design of Polynomial Fuzzy Radial Basis Function Neural Networks (PFRBFNN) based on Nonsymmetric Fuzzy Clustering Method (NSFCM) and PSSA. The PSSA is a parallel optimization algorithm realized by using Hierarchical Fair Competition strategy. NSFCM is essentially an improved fuzzy clustering method, and the good performance in the design of “conventional” Radial Basis Function Neural Networks (RBFNN) has been proven. In the design of PFRBFNN, NSFCM is used to design the premise part of PFRBFNN, while the consequence part is realized by means of weighted least square (WLS) method. Furthermore, HFC-PSSA is exploited here to optimize the proposed neural network. Experimental results demonstrate that the proposed neural network leads to better performance in comparison to some existing neurofuzzy models encountered in the literature.

GT2-CFC: General Type-2 Collaborative Fuzzy Clustering method

2021 ◽  
Author(s):  
Fariba Salehi ◽  
Mohammad Reza Keyvanpour ◽  
Arash Sharifi
Keyword(s):  
Fuzzy Clustering ◽  
General Type ◽  
Clustering Method ◽  
Fuzzy Clustering Method
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