基于极大熵谱估计准则的动态数据预测方法及应用

在进行动态数据预测时，预测结果往往会受人为因素影响而出现偏差。本文将极大熵谱估计准则应用于动态数据预测，详细介绍了趋势项提取、模型参数确定、阶数选择以及数据预报的过程，基于MATLAB平台编制了相应程序，最后结合实例对该方法的优点和不足之处进行了客观评价。     

截面数据的随机成本前沿面模型的半参数分析

针对横截面数据的随机前沿面成本函数，给出了其半参数方法误差密度估计。因为随机前沿面模型中有2个随机误差因子，若用卷积进行密度估计将是非常复杂的?这里用特征函数构造概率密度估计从而避免了应用卷积进行密度估计的复杂操作：但是这种方法也有不足之处，就在于它对模型有一些弱的假定：点估计依赖于误差因子与模型参数的假定，密度估计依赖于误差因子特征函数的假定．     

离散选择模型的比较及其在零售业的应用

离散选择模型可以根据消费者的数据来分析其购买行为,进而为企业营销策略进行决策支持.本文解决了在动态客户关系管理中如何选择合适的离散选择模型这一问题.首先对Logit和Probit模型进行比较研究,其次确定消费者离散选择模型的评价指标,最后在零售业中使用消费者数据对评价指标进行验证.结果表明,所确定的消费者离散选择模型的评价指标可以用来进行模型的选择,Probit模型比Logit模型更适合于零售业的动态客户关系管理问题.同时本文的分析过程也为研究者和企业更好地理解和定量分析消费者购买行为提供了理论参考.     

计量经济模型中的异方差和序列相关问题

通过分析两种违背计量经济模型基本假设的异方差和序列相关问题,以最小二乘法为主要方法,提出多种新型参数检验和参数修正的方法,从而提高计量模型的合理性和预测的有效性.     

基于分布式数据库的挖掘模型

给出了一种基于分布式数据库的挖掘模型及其相应的一种有效的挖掘算法,该模型和算法在减轻网络频繁的通讯负担、体现并行计算以及异构数据挖掘方面有其独特的优点.     

Test Data Sets and Evaluation of Gene Prediction Programs on the Rice Genome

With several rice genome projects approaching completion gene prediction/finding by computer algorithms has become an urgent task. Two test sets were constructed by mapping the newly published 28,469 full-length KOME rice cDNA to the RGP BAC clone sequences of Oryza sativa ssp. japonica: a single-gene set of 550 sequences and a multi-gene set of 62 sequences with 271 genes. These data sets were used to evaluate five ab initio gene prediction programs: RiceHMM, GlimmerR, GeneMark, FGENSH and BGF. The predictions were compared on nucleotide, exon and whole gene structure levels using commonly accepted measures and several new measures. The test results show a progress in performance in chronological order. At the same time complementarity of the programs hints on the possibility of further improvement and on the feasibility of reaching better performance by combining several gene-finders.     

From data to diagnosis and control using generalized orthonormal basis filters. Part I: Development of state observers

This work is aimed at the development of a state observer (steady state Kalman filter) for a multivariable system with unknown time delays, which is subjected to unmeasured disturbances. To design such a filter, we explore the feasibility of capturing system dynamics using generalized orthonormal basis filters (GOBF). A two step identification procedure is proposed by exploiting the fact that the GOBF based models have output error structure. The deterministic component of the model is identified in the first step and used to compute a residual signal. In the second step, a filter that whitens the residuals is estimated using GOBF and combined with the deterministic component. A minimal order state realization of the innovation form of the state model is then generated from this high order model using realization based sub-space based state space (4SID) identification algorithm. When time delays are not known a-priori, the similarity between GOBF and Pade approximation is used to estimates time delay matrix directly from multivariate data. The efficacy of the proposed modeling technique is demonstrated by carrying out simulation studies on the benchmark Shell control problem and experimental evaluation on a stirred tank heater (STH) system. From the analysis of simulation and experimental results, it can be inferred that the proposed approach produces fairly accurate estimates of the time delay matrix and the deterministic and stochastic components of the dynamic model.     

Multiclass P2P networks: Static resource allocation for service differentiation and bandwidth diversity

We propose a multiclass fluid model for BitTorrent-like content-distribution systems. The new model can model heterogeneous peers, in which peers have different access bandwidths. The model can also model BitTorrent-like systems which provide differential service (for example, first class and second class service) to the participating peers. The fluid model leads to a non-linear system of differential equations with special structure. For the service differentiation problem, we prove that the system of differential equations admits a unique stable equilibrium, that we compute in closed-form. We also provide the average download times for both classes. For the bandwidth diversity problem, we show that the system of differential equations has a stable state that may depend on the initial conditions. We compute the average download time of both classes for each reachable steady-state.     

Parameter determination for a generalized fuzzy model

This paper underlines a way to evolve a generalized fuzzy model (GFM), using the interpolation of CRI and TS models in their consequent parts of fuzzy rules. The GFM possesses the index of fuzziness of CRI model and the local model of the TS model. The parameters of the GFM are estimated by a two-step process. The consequent part of fuzzy rules is reformulated to suit the LSE framework for estimating the associated parameters. By assuming Generalized Gaussian membership function for the premise parts, Gradient descent technique is used to update its parameters. The performance of two classes of GFM has been tested on two systems and it is shown that class II GFM is the best out of all the fuzzy models tested.     

Construction and Methods of Learning of Bayesian Networks

Methods of learning Bayesian networks from databases, basic concepts of Bayesian networks, basic methods of learning, methods of learning parameters, and the structures of a network and hidden parameters are considered. Basic definitions and key concepts with illustrative examples are presented. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 133–147, July–August 2005.