Showing papers by "Xiaoou Li published in 2009"
TL;DR: This paper describes a novel nonlinear modeling approach with fuzzy rules and support vector machines that has good accuracy, and is suitable for online fuzzy modeling.
Abstract: This paper describes a novel nonlinear modeling approach with fuzzy rules and support vector machines. Structure identification is realized by an online clustering method and fuzzy support vector machines, the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. The modeling errors are proven to be robustly stable with bounded uncertainties by a Lyapunov method and an input-to-state stability technique. Comparisons with other related works are made through an application of gas furnace process. The results demonstrate that our approach has good accuracy, and this method is suitable for online fuzzy modeling.
18 citations
11 Oct 2009
TL;DR: This paper presents a novel SVMs classification method which reduces significantly the input data set using Bayesian technique and is able to predict with a high accuracy huge data sets in a reasonable time.
Abstract: Support Vector Machines (SVMs) are known to be excellent algorithms for classification problems. The principal disadvantage of SVMs is due to its excessive training time in large data set, such as DNA sequences. This paper presents a novel SVMs classification method which reduces significantly the input data set using Bayesian technique. Using this system, we are able to predict with a high accuracy huge data sets in a reasonable time. The system has been tested successfully on large splice-junction gene sequences (DNA). Experimental results show that the accuracy obtained by the proposed algorithm is comparable (98.2) with other SVMs implementations such as SMO (98.4%), LibSVM (98.4%), and Simple SVM (97.6%). Furthermore the proposed approach is scalable to large data sets with high classification accuracy.
7 citations
13 Nov 2009
TL;DR: An approach based in Petri Net theory is proposed, which detects cyclic paths in the base of ECA rules and can analyze the relationships among ECA rule components.
Abstract: Active database systems integrate event-based rule processing with traditional database functionality. The model most widely used to represent event-based rules is the Event- Condition-Action rule (ECA rule) model. However, the relationships among rules in the development of a base of ECA rules can fall in an infinite rule triggering: the No termination problem. In this article, an approach based in Petri Net theory is proposed. This approach detects cyclic paths in the base of ECA rules. Furthermore, it can analyze the relationships among ECA rule components.
5 citations
14 Jun 2009
TL;DR: In this paper, neural control and SMC are connected serially: first a deadzone neural control assures that the tracking error is bounded, then super-twisting secondorder slidingmode is used to guarantee finite time convergence of the contoller.
Abstract: Combination of neural networks and sliding mode control (SMC) can reduce chattering, because the upper bound of uncertainties becomes smaller when neural networks are used to model unknwn nolinear systems. The tracking error of normal neural sliding mode control is asymptotically stable, while neural control and SMC are applied at same time. In this paper, neural control and SMC are connected serially: first a deadzone neural control assures that the tracking error is bounded, then super-twisting secondorder slidingmode is used to guarantee finite time convergence of the contoller.
3 citations
11 Oct 2009
TL;DR: In this article, the authors introduce potential termination concept which gives valuable information about those rules whose processing may not terminate during execution time and describe a Petri net-based approach to effectively detect termination and potential termination problems.
Abstract: Active rules allow software systems behave automatically when relevant events take place. Due to unstructured rule processing, it is necessary to inspect behavior characteristics such as termination which guarantees that rule processing finishes. In this paper we introduce potential termination concept which gives valuable information about those rules whose processing may not terminate during execution time. It is very useful to manage possible bad scenarios. We also describe our Petri net-based approach to effectively detect termination and potential termination problems.
3 citations
01 Dec 2009
TL;DR: In this paper, a fuzzy cerebellar model articulation controller (CMAC) is used to compensate friction, and gravity, as well as the coupling between position and anti-swing control.
Abstract: This chapter proposes a novel control strategy for overhead cranes. The controller includes both position regulation and anti-swing control. Since the crane model is not exactly known, fuzzy cerebellar model articulation controller (CMAC) is used to compensate friction, and gravity, as well as the coupling between position and anti-swing control. Using a Lyapunov method and an input-to-state stability technique, the controller is proven to be robustly stable with bounded uncertainties. Real-time experiments are presented comparing this new stable control strategy with regular crane controllers.