Regularization theory and neural networks architectures
Neural Computation
An equivalence between sparse approximation and support vector machines
Neural Computation
Practical Aspects of the Moreau--Yosida Regularization: Theoretical Preliminaries
SIAM Journal on Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this study, a novel prediction model, hybrid of mechanism method, Takagi-Sugeno (T-S) fuzzy modeling, regularization network technique and Multi-kernel learning algorithm, is proposed for accurately forecasting the liquid steel temperature in ladle furnace (LF). By virtue of mechanism method and TS fuzzy modeling technique, a partial linear structured mechanism model is firstly obtained, which contains a parametric linear part with unknown coefficients and a non-parametric part with unknown functional expression. Thereafter, it is parameterized and implemented by a modified regularization network, called partial linear regularization network (PLRN), which introduces a parametric linear part into the traditional regularization network. Furthermore, to optimally design the kernel of PLRN and thereby further improve the prediction performance, Multi-kernel learning approach is employed to obtain the so called Multi-kernel learnt PLRN. The principal innovation behind the proposed method is the embedding of the prior knowledge into the model, instead of directly predicting the steel temperature using machine learning techniques which is commonly used in the previous steel temperature prediction models. This innovation leads to better final results in reducing the model complexity, improving the generalization performance and consequently promoting the prediction precise. The experiment results demonstrate that the novel predictor is superior in prediction performance over other black-box based predictors. Furthermore, the prediction accuracy is boosted via Multi-kernel learning.