Self-adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series

As the Box-Jenkins method could not grasp the non-stationary characteristics of time series exactly, nor identify the optimal forecasting model order quickly and precisely, a self-adaptive processing and forecasting algorithm for univariate linear time series is proposed. A self-adaptive series characteristic test framework which employs varieties of statistic tests is constructed to solve the problem of inaccurate identification and inadequate processing for non-stationary characteristics of time series. To achieve favorable forecasts, an optimal forecasting model building algorithm combined with model filter and candidate model pool is proposed, in which a univariate linear time series forecasting model is built. Experimental data demonstrates that the proposed algorithm outperforms the comparativemethod in all forecasting performance statistics.     

单神经元PID控制器在直接矢量控制中的应用研究

异步电机矢量控制改善了电机的转矩控制特性,但是由于系统运行过程中一些不可控或不确定的因素,传统PID控制难以满足精度高、反应快、鲁棒性好的要求。基于单神经元网络设计了用于矢量控制的增益自调整的自适应磁链和速度控制器,并运用改进的学习与控制算法,实现单神经元PID控制器的参数优化与在线自动调整。仿真与实验结果表明：单神经元PID控制器可以改善异步电动机矢量控制的性能,具有较强的自适应性与鲁棒性。     

基于单神经元控制器的异步电动机矢量控制

文中提出了采用单神经元智能控制器代替传统PID控制器以改善异步电动机矢量控制的性能。在分析单神经元控制器结构与控制原理的基础上，为了提高单神经元控制器的学习能力与自适应性，将无监督的Hebb学习规则与有监督的Delta学习规则相结合，运用改进的学习与控制算法，实现单神经元控制器的参数优化与在线自动调整。采用Matlab软件建立单神经元控制器与异步电动机矢量控制模型，进行仿真研究；并将单神经元控制器的控制软件应用于异步电动机矢量系统，进行实验研究。仿真与实验结果表明，单神经元控制器可以改善异步电动机矢量控制的性能，具有较强的自适应性与鲁棒性。     

一种结合改进型ROMF与TDOBA的热图象处理

提出了一种基于统计规律的改进型ＲＯＭＦ与ＴＤＯＢＡ的结合算法，能明显有效地保持热图边缘，具有较好的滤除噪声效果。这是通过基于统计规律的判据，在边缘采用引入熵的改进型的ＲＯＭＦ新算法，而对缓变区采用ＴＤＯＢＡ算法来实现。计算机仿真结果证实了此方法的有效性。     

Self-adaptivity for grid applications. An Efficient Resources Selection model based on evolutionary computation algorithms

Over the last few years, the adaptation ability has become an essential characteristic for grid applications due to the fact that it allows applications to face the dynamic and changing nature of grid systems. This adaptive capability is applied within different grid processes such as resource monitoring, resource discovery, or resource selection. In this regard, the present approach provides a self-adaptive ability to grid applications, focusing on enhancing the resources selection process. This contribution proposes an Efficient Resources Selection model to determine the resources that best fit the application requirements. Hence, the model guides applications during their execution without modifying or controlling grid resources. Within the evaluation phase, the experiments were carried out in a real European grid infrastructure. Finally, the results show that not only a self-adaptive ability is provided by the model but also a reduction in the applications’ execution time and an improvement in the successfully completed tasks rate are accomplished.