A Modified Particle Swarm Optimization and Radial Basis Function Neural Network Hybrid Algorithm Model and Its Application

Shi, Bowen; Li, Yuxia; Yu, Xiangzhan; Yan, Wei; Li, Peng; Meng, Xin

doi:10.1109/gcis.2009.233

Cited by 16 publications

(13 citation statements)

References 2 publications

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“…Although the setting of the basis function centers has been highly addressed by the previous works on RBFNN learning [25][26][27], the learning of the basis function widths has not been much studied. The existing 105 previous works discussed the effect of widths of radial basis functions on performances of classification and function approximation [4,28,29].…”

Section: Computing the Widths Of The Rbfnnmentioning

confidence: 99%

An improved radial basis function neural network for object image retrieval

Montazer

Giveki

2015

Neurocomputing

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Section: Computing the Widths Of The Rbfnnmentioning

confidence: 99%

An improved radial basis function neural network for object image retrieval

Montazer

Giveki

2015

Neurocomputing

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“…The training sets were generated, by selecting 150 samples of (15) in the interval x ∈ [0, 4π] normalized to [0,1]. Also, 500 observations for the test set were sampled without noise.…”

Section: Resultsmentioning

confidence: 99%

“…In [15], a modified version of the PSO was suggested in order to train a RBF network for forecasting the load in a power system, while in [16] the PSO was combined with RBF networks to sale forecasting. In [17], the PSO is used to adapt the backpropagation learning rate, while in [18] an identification of a thermal process is considered.…”

Section: Related Work On Pso and Rbf Networkmentioning

confidence: 99%

On a Multiobjective Training Algorithm for RBF Networks Using Particle Swarm Optimization

Silva

Vieira

Lisboa

et al. 2010

2010 22nd IEEE International Conference on Tools With Artificial Intelligence

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This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machine complexity. The training is done in three steps: i) a conventional minimization of the training error, ii) multiobjective least-squares optimization for the linear parameters and, iii) particle swarm optimization for the nonlinear parameters. Some results are presented and they show the effectiveness of the proposed approach.

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“…Another neural network model which is considered as a good classifier is Radial Basis Function Neural Network (RBFNN) [16]. There are many applications that implemented using RBFNN, such as electroencephalogram (EEGs) classification [17] and weather forecast [18]. In electroencephalogram (EEGs) classification when diagnosing epilepsy produce 93.3% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

Pratiwi

Alexander

Harefa

et al. 2015

Procedia Computer Science

122

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Computer Aided Diagnosis (CAD) is used to assist radiologist in classifying various type of breast cancers. It already proved its success not only in reducing human error in reading the mammograms but also shows better and reliable classification into benign and malignant abnormalities. This paper will report and attempt on using Radial Basis Function Neural Network (RBFNN) for mammograms classification based on Gray-level Co-occurrence Matrix (GLCM) texture based features. In this study, normal and abnormal breast image used as the standard input are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. The computational experiments show that RBFNN is better than Back-propagation Neural Network (BPNN) in performing breast cancer classification. For normal and abnormal classification, the result shows that RBFNN's accuracy is 93.98%, which is 14% higher than BPNN, while the accuracy of benign and malignant classification is 94.29% which is 2% higher than BPNN.

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A Modified Particle Swarm Optimization and Radial Basis Function Neural Network Hybrid Algorithm Model and Its Application

Cited by 16 publications

References 2 publications

An improved radial basis function neural network for object image retrieval

An improved radial basis function neural network for object image retrieval

On a Multiobjective Training Algorithm for RBF Networks Using Particle Swarm Optimization

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

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