分数阶反向累加与累减生成算子及互逆性

采用反向累加与反向累减的信息处理方式,将一阶累加生成算子和一阶累减生成算子拓展至分数阶范围,给出了分数阶反向累加生成算子与分数阶反向累减生成算子的解析表达式,并证明了两者之间的互逆性,为建立基于分数阶反向累加的灰色预测模型和拓宽灰色预测模型的应用范围提供理论基础.     

分数阶灰色累加生成算子与累减生成算子及互逆性

针对现有灰色预测模型主要以一阶累加生成序列作为建模序列,再累减还原为原始序列预测值,本文通过Gamma函数将累加生成算子和累减生成算子拓展到正实数领域,给出分数阶累加生成算子和分数阶累减生成算子的解析表达式,一阶和整数阶均是其特例,证明了两算子之间的互逆性.为建立分数阶灰色预测模型和拓宽灰色预测模型的应用范围提供理论基础.     

基于分数阶累加的灰色GM(1,1|sin)模型及其应用

本文研究了灰色模型对振荡序列的预测问题.在已有GM(1,1|sin)模型的基础上,利用分数阶算子对原始序列进行累加生成的方法,获得了分数阶累加GM(1,1|sin)模型的表达式;以平均相对误差最小化为目标,利用粒子群算法求解非线性优化问题,获得了模型的最优参数.最后以城市交通流的模拟预测为例,结果表明本文提出的模型比GM(1,1|sin)模型具有更高的模拟精度,推广了GM(1,1|sin)预测模型的结果.     

离散分数阶和分算子与差分算子及互逆性

对连续函数进行离散化,给出离散序列的分数阶和分算子与分数阶差分算子的解析表达式,证明了两算子满足交换律、指数率与互逆性.     

基于统一灰色生成算子的三参数离散灰色预测模型及其应用

现有三参数离散灰色预测模型的累加阶数取值范围局限于正实数,导致模型建模能力和作用空间受限。为此,论文首先引入实数域统一灰色生成算子;然后,基于统一灰色生成算子构造了新型三参数离散灰色预测模型,实现了其阶数从正实数到全体实数的拓展与优化,从而使得新型模型具备挖掘时序数据积分特性与差异信息的双重功能;最后,将该新模型应用于某装甲装备维修经费的建模,结果显示其精度优于其它同类灰色模型。本研究成果对完善灰色算子基础理论及提高灰色预测模型建模能力具有重要价值。     

分数阶波方程的数值解法

首先,把分数阶波方程转换成等价的积分-微分方程;然后,利用带权的分数阶矩形公式和紧差分算子分别对时间和空间方向进行离散.证明了当权重为1/2时,时间方向的收敛阶为α,其中α(1α2)为Caputo导数的阶数.利用Gronwall不等式,证明了数值格式的收敛性和稳定性.数值例子进一步表明了数值格式的有效性.     

分数次极大算子的交换子的Sharp加权模不等式

给出了由分数次算子极大算子Mα和b∈BMO生成的m阶交换子的加权范数不等式.并对1p/q≤2的情况举例证明了其结果是最优的.     

基于粒子群优化的分数阶PFGM(1,1)模型在建筑物沉降预测中的应用

针对传统的灰色预测模型对建筑物沉降预测精度不高、拟合数据较差的问题,在传统的GM(1,1)模型基础上提出了分数阶建模的思想,采用粒子群优化算法求解最优分数阶次,建立基于粒子群优化的分数阶PFGM(1,1)模型.实例计算表明,分数阶FGM(1,1)模型可以提高建筑物沉降的预测精度,通过粒子群优化算法选取最优阶次可以进一步提高预测精度和误差检验等级.由此可见,基于粒子群优化的分数阶PFGM(1,1)模型对建筑物的沉降控制有着重要的指导作用.     

含时滞参数的灰色GM(1,1,τ)模型及其应用

针对GM(1,1)模型未能反映系统时滞效应的问题,根据实际应用的需要,利用灰色建模思想构建了含时滞参数7的灰色GM(1,1,Υ)模型,并研究了该模型的建模机理、建模过程,给出参数估计方法.并根据模型的基本形式,构建出以原始值和背景值的一阶累减生成序列的灰色相对关联度最大化为目标的灰色关联分析法来探索时滞参数Υ的确定方法,并获得模型的离散解.最后利用该模型对美国制造业库存总量进行了模拟预测,获得较高的精度,验证了模型的有效性.     

具有预防性维修策略的可修复系统的指数稳定性

利用算子半群理论研究了具有预防性维修策略的可修复系统,通过分析系统算子的谱分布,以及系统算子生成C0半群{T(t)}的本质谱增长阶,证明了C0半群{T(t)}是拟紧半群.同时也证明了该半群还是不可约的.进而得到了可修复可用度的指数稳定性.     

The novel fractional discrete multivariate grey system model and its applications

Fractional order accumulation is a novel and popular tool which is efficient to improve accuracy of the grey models. However, most existing grey models with fractional order accumulation are all developed on the conventional methodology of grey models, which may be inaccurate in the applications. In this paper an existing fractional multivariate grey model with convolution integral is proved to be a biased model, and then a novel fractional discrete multivariate grey model based on discrete modelling technique is proposed, which is proved to be an unbiased model with mathematical analysis and stochastic testing. An algorithm based on the Grey Wolf Optimizer is introduced to optimize the fractional order of the proposed model. Four real world case studies with updated data sets are executed to assess the effectiveness of the proposed model in comparison with nine existing multivariate grey models. The results show that the Grey Wolf Optimizer-based algorithm is very efficient to optimize the fractional order of the proposed model, and the proposed model outperforms other nine models in the all the real world case studies.     

A new method to mitigate data fluctuations for time series prediction

Although the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctuations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new operator can better capture the real developing trend in raw data and improve forecast accuracy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy.     

FPGA Realization of Fractional Order Neuron

In this paper fractional Hindmarsh Rose (HR) neuron, which mimics several behaviors of a real biological neuron is implemented on field programmable gate array (FPGA). The results show several differences in the dynamic characteristics of integer and fractional order Hindmarsh Rose neuron models. The integer order model shows only one type of firing characteristics when the parameters of model remains same. The fractional order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied. The firing frequency increases when the order of the fractional operator decreases. The fractional order is therefore key in determining the firing characteristics of biological neurons. To implement this neuron model first the digital realization of different fractional operator approximations are obtained, then the fractional integrator is used to obtain the low power and low cost hardware realization of fractional HR neuron. The fractional neuron model has been implemented on a low voltage and low power circuit and then compared with its integer counter part. The hardware is used to demonstrate the different dynamical behaviors of fractional HR neuron for different type of approximations obtained for fractional operator in this paper. A coupled network of fractional order HR neurons is also implemented. The results also show that synchronization between neurons increases as long as coupling factor keeps on increasing.     

分数阶累加生成GM~λ(1,1)模型的江苏省农民收入倍增能力预测

江苏省提出居民收入7年倍增计划.那么其农民收入能否同步倍增?基于分数阶累加生成GM~λ(1,1)灰色模型,采用2007—2012年江苏省农民收入数据,对其收入能力进行了预测.经计算发现,在MAPE误差允许范围内,选择分数阶λ值,可使预测结果更合理、更准确;最终结果表明江苏农民只需6年时间其收入就可以倍增.     

分数阶反向累加非线性灰色Bernoulli模型及其应用

非线性灰色Bernoulli模型相对于普通的GM(1,1)模型,能更好的反映数据序列的非线性增长趋势.分数阶蕴含"in between"思想,分数阶累加灰色模型相对一般的累加灰色模型具有更好的预测效果和适应性.为了更好地符合新信息优先原理,实现最小信息的最大挖掘,构造了分数阶反向累加非线性灰色Bernoulli模型,即...     

Fractional Abstract Cauchy Problems

This paper is concerned with fractional abstract Cauchy problems with order \({\alpha\in(1,2)}\). The notion of fractional solution operator is introduced, its some properties are obtained. A generation theorem for exponentially bounded fractional solution operators is given. It is proved that the homogeneous fractional Cauchy problem (FACP ₀) is well-posed if and only if its coefficient operator A generates an α-order fractional solution operator. Sufficient conditions are given to guarantee the existence and uniqueness of mild solutions and strong solutions of the inhomogeneous fractional Cauchy problem (FACP _f ).     

Fractional differential and integral operations via cumulative approach

In this study, a fractal operator model of cumulative processes is described. Accordingly, differential and integral operators of the fractional calculus are derived by the fractal operator model of a cumulative process. In order to exhibit the relation between our cumulative approach and fractional calculus, vertical motion of a body is handled within these frameworks. Thereby, regard to our assessments, the underlying physical mechanism of the success of the fractional differintegral operators in describing stochastic complex systems is uncovered to some extent.     

基于GFM-VAR的我国通用航空产业演进阶段识别及政策强度有效性分析

为揭示通用航空产业系统演化的动态和有序规律,挖掘产业形成和可持续发展的阶段识别证据,针对产业演化数据的信息不完全和连续聚类特性,基于灰色系统理论和最优分割理论构建通用航空产业演进阶段识别的灰色生成序列最优分割模型(Grey sequence generation-Fisher Model,GFM),并运用VAR模型分析通航产业政策强度的有效性。首先运用灰色系统理论的(位置加权)几何平均强化缓冲算子(W)GASBO((Weight)Geometry average strengthening buffer operator)扩张演化数据波形特征并生成灰色序列矩阵;运用最优分割理论的组内离差平方和最小判断原则判定通用航空产业演化的有序递进阶段;然后,通过我国2004~2013年通用航空产业演进阶段的实证研究,发现:我国通用航空产业演化以2010年为分界点,先后经历了初创和成长两个阶段;基于VAR模型的脉冲响应函数确定我国通航产业政策强度受“倒逼出台”、“时滞限制”的影响呈弱有效性;基于政策强度(WGASBO算子)的灰色生成序列最优分割模型更有效、更具有可行性。     

α-因子缓冲算子的构造及其性质的研究

为了解决灰色预测模型中的随机挠动序列问题,本文在灰色系统理论以及灰色预测建模技术的基础上,通过梳理历史文献的研究结果,提出了一个具有缓冲作用强度可调整的新型缓冲算子,即α-因子缓冲算子,该算子具有强度可调整、形式统一、预测结果优良等特性.具体来说就是通过调整强度系数可以改变其缓冲作用的强度大小、形式上兼顾了弱化缓冲算子和强化缓冲算子的统一性、在一定范围内的应用中预测结果优于其他的缓冲算子.实证的结果的确显示出了这些优良性质.