Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

Abstract: After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks isintroduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulatederrors produced in the recursive process. Characterized by predictive control, this method can achieve a good controlaccuracy and has good robustness. A simulation study shows that this control algorithm is very effective.     

一种评定实验教学质量与学习效果的方法研究

本文分析了实验教学评价的特殊性、复杂性与重要性。介绍了运用权重法评定实验教学效果的基本步骤方法。设计了用权重法评定实验教学质量与学习效果的两例评估计算方法。提出了还可以根据实验教学的实践性特点,用权重法设定专项评价和制作自动评价系统课件的设想。     

网络技术与伦理道德矛盾的化解

信息网络的发展，促进了社会生产、生活的深刻变革，同时也产生了一系列的网络技术与伦理道德之间的矛盾。网络道德具有自主性、开放性和多元性的特点，化解网络技术与伦理道德矛盾的对策主要包括加强网络主体的道德自律、加强社会主义网络体制改革、加强多元网络道德原则的支持三个方面。     

Subset threshold autoregression

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright © 2003 John Wiley & Sons, Ltd.     

攀枝花市人才发展对策研究

本文通过对攀枝花市人才状况进行调研,针对攀枝花市人才队伍现状以及人才资源管理中存在的问题,结合攀枝花市未来发展规划及实际,提出攀枝花市人才开发的对策。     

浅谈如何做好群众工作

通过回顾党的历史上关于群众工作的一系列决策决定,指出领导干部要进一步提高对做好群众工作重要性的认识;对做好群众工作中领导干部的要求做出阐述。     

网格技术发展现状研究

本文阐述了网格的概念、基础理论、国外一些国家的网格研究计划,并在此基础上对国内的研究工作提出了一些建议。     

经典遗传学为什么崛起于美国?——评美、德的不同社会环境对遗传学的影响

本文对经典遗传学产生的社会和历史渊源作出了社会学的和思想的考察,并加以深入的分析。     

S桶空间的几个性质(Ⅱ)

讨论了S桶空间在归纳限和稠密子空间方面的一些基本性质．