Improved Fuzzy C-Means Clustering Algorithm Based on Particle Swarm Optimization Algorithm

2016 ◽  
pp. 617-623 ◽  
Author(s):  
Quan Hu ◽  
Kai Zheng ◽  
Zheng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Research on Algorithm Optimization of Hidden Units Data Centre of RBF Neural Network

2013 ◽  
Vol 831 ◽  
pp. 486-489 ◽  
Author(s):  
Jing Ying Zhao ◽  
Hai Guo ◽  
Xiao Niu Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Subtractive Clustering ◽  
Rbf Neural Networks ◽  
Algorithm Optimization ◽  
Swarm Optimization

Common algorithms of selecting hidden unit data center in RBF neural networks were first discussed in this essay, i.e. k-means algorithm, subtractive clustering algorithm and orthogonal least squares. Meanwhile, a hybrid algorithm mixed of k-means algorithm and particle swarm optimization algorithm was put forward. The algorithm used the position of the particles in particle swarm optimization algorithm to help deal with the defects of local clusters resulted from k-means algorithm and to make optimization with the optimal fitness of k-means particle swarm with the aim to make the final optimal fitness better satisfy the requirements.

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