The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A tutorial on support vector regression
Statistics and Computing
Support vector machine for functional data classification
Neurocomputing
Robust clustering by pruning outliers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
IEEE Transactions on Fuzzy Systems
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Robust support vector regression networks for function approximation with outliers
IEEE Transactions on Neural Networks
Active set support vector regression
IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems
IEEE Transactions on Fuzzy Systems
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
Smooth support vector learning for fuzzy rule-based classification systems
Intelligent Data Analysis
TS-fuzzy modeling based on ε-insensitive smooth support vector regression
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper proposes a new regression model, the Takagi-Sugeno Fuzzy System-based Support Vector Regression (TSFS-SVR). The TSFS-SVR is motivated by TS-type fuzzy rules and its parameters are learned by a combination of fuzzy clustering and linear SVR. In contrast to a kernel-based SVR, the TSFS-SVR has a smaller number of parameters while retaining the SVR's good generalization ability. In TSFS-SVR, a one-pass clustering algorithm clusters the input training data. A new TS-kernel, which corresponds to a TS-type fuzzy rule, is then constructed by the product of a cluster output and a linear combination of input variables. The TSFS-SVR output is a linear weighted sum of the TS-kernels. To achieve high generalization ability, TSFS-SVR weights are learned through linear SVR. This paper demonstrates the capabilities of TSFS-SVR by conducting simulations in clean and noisy function approximations and signal prediction. This paper also compares simulation results from the TSFS-SVR with Gaussian kernel-based SVR and other learning models.