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Using Greedy algorithm: DBSCAN revisited II

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Journal of Zhejiang University Science 2004年第11期5卷 1405-1412页

作者：岳士弘李平郭继东周水庚 instituteofindustrialprocesscontrol ZhejiangUniversityHangzhou310027China YiliTeacher＇sCollege Yining835000China

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.

关键词： DBSCAN algorithm Greedy algorithm Density-skewed cluster

Fuzzy NN based predictive control and its application to green liquor system

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Journal of Zhejiang University Science 2002年第1期3卷 57-59页

作者：李江周伟张良军李平 instituteofindustrialprocesscontrol ZhejiangUniversityHartgzhou310027China CollegeofElectricalEngineering ZhejiangUniversityHartgzhou310027China

The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.

关键词： model predictive control fuzzy neural networks generalized predictive control

Robust predictive control of uncertain intergrating linear systems with input constraints

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Journal of Zhejiang University Science 2002年第4期3卷 418-425页

作者：张良军李江宋执环李平 instituteofindustrialprocesscontrol ZejiangUniversityHangzhou310027China

This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.

关键词： Model predictive control Robust control Input constraints Convex programming

SVM CLASSIFICATION:ITS CONTENTS AND CHALLENGES

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Applied Mathematics(A Journal of Chinese Universities) 2003年第3期18卷 332-342页

作者： YueShihong LiPing HaoPeiyi instituteofindustrialprocesscontrol ZhejiangUniv.Hangzhou310027China. Dept.ofComputerSei.andinform.Eng. ChengKungUniv.TaiwanChina.

SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification,regression or novelty *** particular,they exhibit good generalization performance on many real issues and the approach is properly motivated *** are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by *** this paper,survey of the key contents on this subject,focusing on the most well-known models based on kernel substitution,namely SVM,as well as the activated fields at present and the development tendency,is presented.

关键词： kernel methods mathematical programming SVM

Scheduling and control of failure prone systems with demand uncertainty and job overlaps

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控制理论与应用（英文版） 2004年第4期2卷 345-352页

作者： JunLIU PingLI ZhihuanSONG instituteofindustrialprocesscontrol ZhejiangUniversityHangzhouZhejiang310027China instituteofindustrialprocesscontrol ZhejiangUniversityHangzhouZhejiang310027China／／SchoolofMechanical＆ElectonicalEngineeringLanzhouUniversityofTechnologyLanzhouGansu730050China

Given that the overlapping of jobs is permitted, the paper studies the scheduling and control of failure prone production systems, i.e. so-called settings with demand uncertainty and job overlaps. Because a variable demand resource is involved in the production and corrective maintenance control problems of the system, which switched randomly between zero and a maximum level, it is difficult to obtain the analytical solutions of the optimal single hedging point policy. An asymptotic optimal scheduling policy is presented and a double hedging point policy is offered to control simultaneously the production rate and the corrective maintenance rate of the system. The corresponding analytical solutions and approximate solutions are obtained. Considering the relationship of production, corrective maintenance and demand variable, an approximate optimal single hedging point control policy is proposed. Numerical results are presented.

关键词： Overlap Hybrid system Markov processes Optimal hedging point policy Production control Corrective maintenance