基于径向基的瓦斯涌出量灰色预测模型

为了进一步预防煤层瓦斯突出,实现准确、快速预测煤矿瓦斯涌出量的大小,首先采用1-AGO对样本数据进行处理,建立灰色(GM)预测模型,再利用径向基(RBF)神经网络对灰色预测模型结果进行预测,以作为其最终的预测值;采用阜新煤矿某工作面瓦斯涌出量的历史数据进行建模,实验结果表明,GM-RBF组合模型在预测精度及训练误差方面均优于单一的GM模型和RBF神经网络预测模型;算法计算简便,减弱了数据的随机性及模型误差,煤矿瓦斯涌出量的预测平均误差减小到1.57%。     

Hybrid function projective synchronization of chaotic systems with uncertain time-varying parameters via Fourier series expansion

In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.     

Exponential stabilization of uncertain time‐delay linear systems with Markovian jumping parameters

This paper studies the exponential stabilization problem of uncertain time‐delay linear systems with Markovian jumping parameters. A novel delay decomposition approach is developed to derive delay‐dependent conditions under which the closed‐loop control system is mean square exponentially stable for all admissible uncertainties. It is shown that the feedback gain matrices and the decay rate can be obtained by solving coupled linear matrix inequalities. Moreover, the difficulties arising from searching for tuning parameters in the existing methods are overcome. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

空间绳系机器人的多次推力逼近策略研究

针对空间绳系机器人近距离逼近问题,提出了基于时间最优的一般N次推力机动策略及算法,由演化得到的等时间N次机动模式和等速度增量N次推力机动模式,能够将多次推力机动策略中复杂的多时间变量求值问题转化为有非线性约束的最小机动时间问题,并利用基于罚函数法的遗传算法进行求解,对空间绳系机器人的近距离逼近问题进行了仿真。仿真表明:一般N次推力机动策略及算法能够很好解决此类多时间变量求值问题;且不需要进行初始速度脉冲修正,相对于基于"时间倒流法"的N次推力策略和连续推力具有较小的机动时间,并在某些情形下相较于后两者以及双冲量机动具有较少的能耗或者较小的最大视界角,为绳系机器人推动逼近设计提供了依据。     

组合模型在降雨量预测中的应用

研究降雨量准确预测问题,降水量的变化既受大气环流、地形、气压、气候带等各种环境因子的影响,降水量的动态特征呈现复杂非线性和各种干扰因素,预测不可能准确。传统预测模型难以对其进行准确预测,预测精度低。为提高降雨量的预测精度,提出一种组合模型的降雨量预测模型。首先采用小波分析将降雨量数据进行分解成线性和非线性部分,然后分别采用ARIMA和RBF神经网络模型对其进行预测,最后采用小波重构线性和非线性预测结果,得到降雨量最终预测结果。仿真结果表明,相对于传统预测模型,组合模型提高了降雨量预测精度,预测结果可以帮助农业、水利部门提高防治旱涝灾害的科学依据。     

时间相关性经验知识与SVM的融合方法研究

时序数据在时间维度上存在着很强的时间相关性,在时序预测中,利用时序数据的时间相关性特点,构造了一种适用于时序数据预测的时序核函数,实现了将时间相关性融合于支持向量机,并通过人工数据和真实数据验证了时序核函数解决时序预测问题的有效性,并与传统核函数相比具有较好的泛化能力。     

基于多条件约束的无线传感器网络QoS路径选择算法

多约束QoS路径选择是无线多媒体传感器网络亟待解决的关键问题,已被证明是NP全问题。分析了有线传感器网络使用的多项式时间和伪多项式时间启发式路径选择算法存在着计算复杂度高的特点,不适合无线多媒体传感器网。提出了一种基于改进的模拟退火的多约束QoS路径选择算法,选取控制参数T的衰减函数和控制参数T的终值Tf这两个重要参数进行优化,构造出更精细的冷却进度表。分析表明该算法是一种高效的路径选择算法,在不牺牲算法复杂度的情况下,能够提高最终解的质量。     

引入漂移特性的用户兴趣模型优化研究

根据用户浏览网页时的操作行为,通过量化的方法建立起用户兴趣模型来反映用户兴趣,从而针对不同用户推荐其可能感兴趣的文章。基于兴趣模型的更新效率问题和用户兴趣的漂移特性,引入兴趣模型的时间分段机制和时间衰减机制,对兴趣模型进行了持续优化。实验表明,优化的兴趣模型在系统性能上有较大的提升,并能较好地反映出用户的兴趣变化,对于用户兴趣的表征更加准确,从而进一步提高了兴趣模型推荐文章的准确率。     

基于相似样本和多模型动态最优组合的光伏功率预测

针对传统单一预测方法存在的局限性,引入了考虑特征加权的模糊聚类方法,进行关于天气类型的划分以得到相似样本;提出多模型动态最优组合预测方法,根据各窗口期预测误差的波动情况,设置合适的临近历史样本窗口宽度,利用窗口期中的数据和构建的最优赋权模型进行组合权重的求解,在避免单一预测方法片面性的同时,提高了对各种天气的适应性。通过算例验证分析表明,所提出的组合预测方法在各种天气类型下的预测效果都优于理论预测、BP预测和LSSVM预测等单一预测方法,能够有效提高预测的有效性和准确性,具有较高的工程实用价值。