数控系统误差补偿新方法研究

1　Introduction　Improvementofmachinetoolaccuracyhasbecomeincreasinglyimportantinmodernmanufacturing .On lywithhighaccuracyofthemachinetoolcanpartswithhighaccuracybeproduced .Therearetwomainapproachestoachieveaccuracyofthemachinetool:erroravoidanceanderrorcompensation .Erroravoid ancetechniquesareusuallyimplementedatthema chinedesignandmanufacturingstages .Becauseofthetechnicalandeconomicallimitations,therearealwayssomeresidualerrors .Therefore ,errorcompensationbecomesanecessaryandpracticals…     

Genetic programming-based chaotic time series modeling

INTRODUCTION Chaos is based on the principle that simpledeterministic laws can exhibit complex externalbehavior (Hegger and Kantz, 2000); although it isquite difficult to reveal such simple laws from thesystem’s external behavior only. This is mainly dueto the dissipation and sensitivity to initial condi-tions (SIC) properties of the chaotic systems (Weiet al., 2002). Linear system theory has developedfor many years, but has proved to be incapable ofsufficiently modeling the chaot…     

Optimal bandwidth scheduling of networked control systems （NCSs） in accordance with jitter

INTRODUCTION Compared with point-to-point control, the major advantages of networked control (NC) include flexi-bility of operation, integrated diagnostics, quick and easy maintenance and monitoring, small wiring volume, low cost. There are also many disadvantages due to the limited bandwidth resources shared by all nodes. Two major consequences are the net-work-induced delay and the jitter, the transmission fluctuations of successive instances of a data, both of which can degrade the dy…     

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling(GPM)algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space,and the Particle Swarm Optimization(PSO)algorithm is used for Nonlinear Parameter Estimation(NPE)of dynamic model structures. In addition,GPM integrates the results of Nonlinear Time Series Analysis(NTSA)to adjust the parameters and takes them as the criteria of established models.Experiments showed the effectiveness of such improvements on chaotic time series modeling.