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Hou C.,Institute of Intelligent Information Processing | Jiao L.,Institute of Intelligent Information Processing
Pattern Recognition Letters | Year: 2010

This paper presents two embedded feature selection algorithms for linear-chain CRFs named GFSA_LCRF and PGFSA_LCRF. GFSA_LCRF iteratively selects a feature incorporating which into the CRF will improve the conditional log-likelihood of the CRF most at one time. For time efficiency, only the weight of the new feature is optimized to maximize the log-likelihood instead of all weights of features in the CRF. The process is iterated until incorporating new features into the CRF can not improve the log-likelihood of the CRF noticeably. PGFSA_LCRF adopts pseudo-likelihood as evaluation criterion to iteratively select features to improve the speed of GFSA_LCRF. Furthermore, it scans all candidate features and forms a small feature set containing some promising features at certain iterations. Then, the small feature set will be used by subsequent iterations to further improve the speed. Experiments on two real-world problems show that CRFs with significantly fewer features selected by our algorithms achieve competitive performance while obtaining significantly shorter testing time. © 2009 Elsevier B.V. All rights reserved. Source


Li C.,Institute of Intelligent Information Processing | Li C.,Guizhou University | Long F.,Institute of Intelligent Information Processing | Long F.,Guizhou University | And 3 more authors.
ISSCAA2010 - 3rd International Symposium on Systems and Control in Aeronautics and Astronautics | Year: 2010

The state feedback robust H-infinity control problem is investigated for discrete-time state delay switched linear descriptor systems with exponential uncertainties in this note. Aiming at the effect of exponential uncertainties, we first treats the uncertain exponential terms as polynomial uncertainty with an additive norm bounded uncertainty based on Taylor series approximation and convex polytope technique, and then a switching strategy and state feedback sub-controllers design are stated to guarantee the H-infinity performance of the whole discrete-time switched linear descriptor system with state delay by using multiple Lyapunov function technology and LMI approach. Finally, a numerical example is presented to illustrate our results. ©2010 IEEE. Source


Long F.,Institute of Intelligent Information Processing | Li C.,Institute of Intelligent Information Processing | Li C.,Guizhou University | Cui C.,Institute of Intelligent Information Processing | Cui C.,Guizhou University
International Journal of Innovative Computing, Information and Control | Year: 2010

The dynamic output feedback robust H-infinity control problem is investigated for switched linear systems with exponential uncertainties in this note. Aiming at the effect of exponential uncertainties, we firstly treats the uncertain exponential terms as polynomial uncertainty with an additive norm bounded uncertainty based on Taylor series approximation and convex polytopic technique. Secondly, a switching strategy and dynamic output feedback controllers are designed to guarantee the H-infinity performance of whole switchrd system by using switched Lyapunov function technology and LMI approach. Finally, a numerical example is presented to illustrate our results. © 2010 ISSN. Source


Gong M.,Institute of Intelligent Information Processing | Jiao L.,Institute of Intelligent Information Processing | Zhang L.,Institute of Intelligent Information Processing
Information Sciences | Year: 2010

Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of clonal selection and hypermutation, and known as clonal selection algorithms. These clonal selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect, an improved clonal selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, clonal proliferation, Baldwinian learning, hypermutation, and clonal selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization. © 2009 Elsevier Inc. All rights reserved. Source


Chen W.,Key Laboratory of Intelligent Perception | Jiao L.,Institute of Intelligent Information Processing
IEEE Transactions on Neural Networks | Year: 2010

This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief. © 2009 IEEE. Source

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