Best estimated inverse versus inverse of the best estimator
Neural Networks
Incremental training of support vector machines using hyperspheres
Pattern Recognition Letters
ANN inverse analysis based on stochastic small-sample training set simulation
Engineering Applications of Artificial Intelligence
Robust ASR using Support Vector Machines
Speech Communication
Predictive Modeling with Echo State Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
The agile improvement of MMORPGs based on the enhanced chaotic neural network
Knowledge-Based Systems
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Segmentation of DNA using simple recurrent neural network
Knowledge-Based Systems
Simple instance selection for bankruptcy prediction
Knowledge-Based Systems
Support Vector Echo-State Machine for Chaotic Time-Series Prediction
IEEE Transactions on Neural Networks
Identification and predictive control for a circulation fluidized bed boiler
Knowledge-Based Systems
Hi-index | 0.00 |
In this paper, we present a new method based on echo state network (ESN) to control discrete chaotic systems. ESN could achieve very high precision in chaotic time series prediction and overcome most issues encountered in using traditional artificial neural networks, especially local minima and overfitting. In order to achieve good control effect when there is noise in chaotic systems, an adaptive noise canceler is introduced to eliminate the effect of the noise and perturbation. The support vector machine (SVM) is adopted to identify inverse model of the controlled plant as the adaptive noise canceler. Simulation results show that the proposed method could achieve very good control effect, possess a good stability and completely reduce the adverse effect.