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IEEE Transactions on Systems, Man, and Cybernetics, Part B. 01/2012; 42:69-80.
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ABSTRACT: This paper introduces a new structure of radial basis function networks (RBFNs) that can successfully model symbolic interval-valued data. In the proposed structure, to handle symbolic interval data, the Gaussian functions required in the RBFNs are modified to consider interval distance measure, and the synaptic weights of the RBFNs are replaced by linear interval regression weights. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the center and range of the interval-valued data are considered. In addition, in the proposed approach, two stages of learning mechanisms are proposed. In stage 1, an initial structure (i.e., the number of hidden nodes and the adjustable parameters of radial basis functions) of the proposed structure is obtained by the interval competitive agglomeration clustering algorithm. In stage 2, a gradient-descent kind of learning algorithm is applied to fine-tune the parameters of the radial basis function and the coefficients of the linear interval regression weights. Various experiments are conducted, and the average behavior of the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment are considered as the performance index. The results clearly show the effectiveness of the proposed structure.
IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 08/2011; 42(1):69-80. · 3.01 Impact Factor
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ABSTRACT: In this paper, least trimmed squares (LTS) based CPBUM neural networks are proposed to improve the outliers and noise problems of conventional neural networks. In general, the obtained training data in the real applications maybe contain the outliers and noise. Although the CPBUM neural networks have fast convergent speed, this model is difficult to deal with training data set with outliers and noise. Hence, the robust property must be enhanced for the CPBUM neural networks. In this paper, the LTS computational architecture is proposed for the CPBUM neural networks. That is, the LTS approach can trim some large noise and outliers in the training data set. After the LTS, the gradient-descent kind of learning algorithms is used as the learning algorithm to adjust the weights of the CPBUM neural networks. It tunes out that the LTS based CPBUM neural networks have fast convergent speed and robust against outliers and noise than the conventional neural networks with robust mechanism. Simulation results are provided to show the validity and applicability of the proposed neural networks.
System Science and Engineering (ICSSE), 2011 International Conference on; 07/2011
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ABSTRACT: 1 In this study, a novel robust clustering algorithm, robust interval competitive agglomeration (RICA) clustering algorithm, is proposed to overcome the problems of the outliers, the numbers of cluster and the initialization of prototype in the fuzzy C-means (FCM) clustering algorithm for the symbolic inter-val-values data. In the proposed RICA clustering al-gorithm, the Euclidean distance measure is consid-ered. Due to the competitive agglomeration is used, the RICA clustering algorithm can be fast converges in a few iterations and to the same optimal partition regardless of its initialization of prototype. Experi-mentally results show the merits and usefulness of the RICA clustering algorithm for the symbolic inter-val-values data with outliers.
International Journal of Fuzzy Systems 10/2010; 12. · 1.16 Impact Factor
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ABSTRACT: In this paper, a Box-Cox transformation-based annealing robust radial basis function networks (BCT-ARRBFNs) is proposed for training data set with skewness noise. Firstly, the initial structure is determined by a fixed BCT-ARRBFNs model which is derived by support vector regression (SVR). Secondly, the results of the SVR are used as the initial parameters of structure in the fixed BCT-ARRBFNs. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed BCT-ARRBFNs and applied to adjust the parameters and weights. The BCT-ARRBFNs is more generalized radial basis function networks model which has fast convergence speed and is robust against heteroscedasticity noises and outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with the BCT-ARRBFNs model.
System Science and Engineering (ICSSE), 2010 International Conference on; 08/2010
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Expert Syst. Appl. 01/2010; 37:6567-6578.
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FUZZ-IEEE 2010, IEEE International Conference on Fuzzy Systems, Barcelona, Spain, 18-23 July, 2010, Proceedings; 01/2010
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Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 10-13 October 2010; 01/2010
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Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 10-13 October 2010; 01/2010
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FUZZ-IEEE 2009, IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 20-24 August 2009, Proceedings; 01/2009
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FUZZ-IEEE 2009, IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 20-24 August 2009, Proceedings; 01/2009
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ABSTRACT: In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FCM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFCMwUNC clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FCM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFCMwUNC clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFCMwUNC clustering algorithm for the symbolic interval-values data.
SICE Annual Conference, 2008; 09/2008
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ABSTRACT: In this study, the robust least square support vector machines for regression (RLS-SVMR) is proposed to deal with training data set with outliers. There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector regression (SVR) approach is used to filter out the outliers in the training data set. Due to the outliers in the training data set are removed, the concepts of robust statistic theory have no need to reduce the outlierpsilas effect. Then, the training data set except for outliers, called as the reduced training data set, is directly used to training the non-robust least squares support vector machines for regression (LS-SVMR) in the stage II. Consequently, the learning mechanism of the proposed approach is much easier than the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach is superior to the weighted LS-SVMR approach when the outliers are existed.
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on; 07/2008
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ABSTRACT: In this study, the hybrid support vector machines for regression (HSVMR) is proposed to deal with training data set with outliers for fuzzy neural networks (FNNs). There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the outliers in the training data set. Due to the outliers in the training data set are removed, the concept of robust statistic theory have no need to reduce the outlierpsilas effect. Then, the training data set except for outliers, called as the reduced training data set, is directly used to training the sparse least squares support vector machines for regression (LS-SVMR) in the stage H. Consequently, the learning mechanism of the proposed approach for fuzzy neural network does not need iterated learning for simplified fuzzy inference systems. Based on the simulation results, the performance of the proposed approach is superior to the robust LS-SVMR approach when the outliers are existed.
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on; 07/2008
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ABSTRACT: In this paper, the annealing robust fuzzy neural networks (ARFNNs) are proposed to improve the problems of fuzzy neural networks for modeling of molecular biology systems with outliers. Firstly, the support vector regression (SVR) approach is proposed to determine the initial structure of ARFNNs. Because of a SVR approach is equivalent to solving a linear constrained quadratic programming problem under a fixed structure of SVR the number of hidden nodes, the initial parameters and the initial weights of ARFNNs are easy obtained via the SVR approach. Secondly, the results of SVR are used as initial structure in ARFNNs. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for ARFNNs, and applied to adjust the parameters in the membership function as well as weights of ARFNNs. That is, an ARLA is proposed to overcome the problems of initialization and the cut-off points in the robust learning algorithm. Hence, when an initial structure of ARFNNs are determined by a SVR approach, the ARFNNs with ARLA have fast convergence speed and robust against outliers for the modeling of molecular biology systems.
Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on; 11/2007
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ABSTRACT: In this paper, we integrate the techniques of cerebellar model articulation controller with general basis function (CMAC-GBF) and support vector regression (SVR) to develop a more efficient scheme. The advantages of CMAC-GBF include: fast learning speed, guarantee learning convergence, capability of derivative, etc. On the other hand, a SVR is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory and has robust properties that against noise. In this paper, we propose the SVR-based CMAC-GBF systems that combined SVR with CMAC-GBF systems. From the results of simulation, the proposed structure has high accuracy and noise against. Besides, the experimental testing results demonstrate that the SVR-based CMAC-GBF systems outperform the original CMAC-GBF systems.
Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International; 08/2007
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ABSTRACT: Bioinformatics is the computing response to the molecular revolution in biology. This revolution has reshaped the lift sciences and given us a deep understanding of DNA sequences, RNA synthesis and the generation of proteins. This process can be represented as gene expression of molecular autoregulatory feedback loop systems. In this paper, the annealing robust fuzzy basis function (ARFBF) is proposed to improve the problems of fuzzy basis function for modeling of gene expression of molecular autoregulatory feedback loop systems with outliers. Firstly, the support vector regression (SVR) approach is proposed to determine the initial structure of ARFBF. Because of a SVR approach is equivalent to solving a linear constrained quadratic programming problem under a fixed structure of SVR, the number of hidden nodes, the initial parameters and the initial weights of ARFBF are easy obtained via the SVR approach. Secondly, the results of SVR are used as initial structure in ARFBF. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for ARFBF, and applied to adjust the parameters as well as weights of ARFBF. That is, an ARLA is proposed to overcome the problems of initialization and the cut-off points in the robust learning algorithm. Hence, when an initial structure of ARFBF is determined by a SVR approach, the ARFBF with ARLA have fast convergence speed and robust against outliers for the modeling of molecular autoregulatory feedback loop systems.
Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International; 08/2007
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Appl. Soft Comput. 01/2007; 7:957-967.
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ABSTRACT: This paper studies an intelligent system, including a support vector regression (SVR) and a similar analysis, for the classification of the ovarian cancer with microarray data. That is, steps in the classification include a feature selection step and a distance measure step. Firstly, the SVR is used to do the feature selection. That is, the SVR is applied to obtain the important genes of ovarian cancer for all samples of microarray data. At the same time, we can compute the frequency of the gene selection based on the results of SVR for all samples to determine the target genes of ovarian cancer. Secondly, the distance under the similar analysis between target data and test data can be determined. From the distance results, the classification of ovarian cancer can easy to determine
Systems, Man and Cybernetics, 2005 IEEE International Conference on; 11/2005
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ABSTRACT: In general, the support vector regression (SVR) is very suitable to approximate a high dimensionality space and ill-posed problem in modeling. That is, the SVR consists of a quadratic programming problem that can be solved efficiently and guaranteed to find a global extremism. Therefore, for the complex data, the SVR is easy to reconstruct an approximated model based on the linear programming technique. On the other hand, a typical microarray data consists of expression levels for a large number of genes on a relatively small number of samples. In order to avoid higher computational complexity and larger prediction errors on high-dimensional problem, we proposed the dimension reduction with SVR for the ovarian cancer microarray data. The SVR can reduce dimension on each sample from 9600 genes to about three hundreds genes. Besides, we can choose the epsiv value in the loss-function of SVR to obtain the variable number of gene and the proposed method can also overcome the block effect of microarray data. Finally, these results can provide for gene class discovery and gene class prediction
Systems, Man and Cybernetics, 2005 IEEE International Conference on; 11/2005
