一类非线性时变结构系统的辨识方法和预报器

本文对一类非线性时变结构系统,给出一种新型的递推梯度法和结构适应式自校正递推预报器.这种算法能自动改变模型的结构.通过油田预报实践,表明这种预报器能跟踪系统结构性变化,从而提高变结构系统预报的精度.     

天气系统的建模与预报

本文讨论了天气系统预报模型的建模和预报问题,说明了由于有了多层递阶预报方法,所以使得具有时变参数的线性预报模型在预报问题中具有一定的普遍性,并给出了应用效果的报告。     

多模型自适应预报及其在电力规划负荷预报中的应用

本文提出一种多模型自适应预报方法,预报系统由几个并行的多步预报器组成,最终预报由Bayes决策律决定。本法适用于随机快时变参数动态过程的预报,在我国工业用电量长期预报中应用此法获得了良好的效果。     

时滞系统的多步预报控制

本文针对具有模型不确定性的时变大时滞系统，提出一种带监测及切换系统的多步预报控制算法。该方法的主要设计思想是对系统在包括时滞范围在内的未来时刻的输出作出预报，依此预报值进行滚动优化和闭环极点配置，同时，增加一层监测及切换系统，以保证整个系统的稳定性。理论分析及仿真实验表明，该方法能有效地用于时变大时滞对象的控制，是一种鲁棒性好的控制算法。     

双层自校正预报方法及其在高炉铁水含硅量预报中的应用

针对象高炉这样复杂的 MISO 系统,本文提出了一种双层自校正预报方法,将快时变部分的递推辨识和慢时变部分的迭代修正相结合,较好地解决了各子模型时间常数大小和时变性快慢相差较大所带来的问题,并在高炉铁水含硅量预报的实际应用中获得了满意的结果.     

一类结构时变SISO系统的自适应控制

本文考虑了一类结构时变SISO系统的自适应控制问题,讨论了模型结构状态逗留时间的确定方法,在此基础上给出结构时变系统的自适应控制律。     

动态系统时变参数预报的一种途径

对于动态系统进行控制的前提是对于系统的状态进行估计和预报。直接做这项工作往往比较困难,因为系统的数学模型中含有未知时变参数。对于未知的时变参数进行估计已有许多方法。本文采用古典函数逼近方法,即用多项式函数来估计和预报时变参数。这种方法简便易行,如果能取得大量的观测数据,多项式函数的阶数又取得较高,就即保证了时变参数的预报精度,又减少了工作量。如果采用样条函数来逼近,效果会更佳。对于线性及非线性系统本文均做了讨论,并用模拟例子说明了这种方法是实用的。     

动态系统时变参数的辨识

本文给出了跟踪动态系统时变参数的一种简单而有效的算法,引出了多输出系统输出可 分离性的概念,说明在辨识的过程中,在适当条件下,n输出系统可分解成n个一定意义下与 之等效的单输出系统,这种方法将给时变参数的辨识带来方便.本文的结果主要应用于预报 模型的辨识.     

模型结构未知的多模型系统辨识的一种简化途径

本文考虑多模型系统,包括模型结构时变系统的辨识问题。这里不假定模型结构已知,给出了多模型系统模型族的统一描述方法,包括模型结构与参数的辨识方法。并对所提出的方法进行了理论分析和计算机仿真。     

基于多Agent的洪水预报系统研究

在分析洪水预报方法及研究进展的基础上,提出了一种基于多Agent(Multi-Agent)新型体系结构的洪水预报系统模型,给出了系统的整体结构及预报节点MAS结构.简要介绍了界面Agent、管理Agent、信息Agent的地位、功能和设计方法,并详细研究了预报Agent的结构、算法设计.针对所提出的系统模型及预报方法,结合渭河流域实例数据进行了预报与分析.结果表明了该系统的有效性.     

Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.     

A new intelligent prediction system model-the compound pyramid model

A current development trend in research on intelligent systems is to optimize a general intelligent prediction system into an individuation intelligent prediction system that is applied in specialized fields. Protein structure prediction is a challenging international issue. In this paper, we propose a new intelligent prediction system model, designed as a multi-layer compound pyramid model, for predicting secondary protein structure. The model comprises four independent intelligent interfaces and several knowledge discovery methods. The model penetrates throughout the domain knowledge, with the effective attributes chosen by Causal Cellular Automata. Furthermore, a high pure structure database is constructed for training. On the RS126 dataset, the overall state per-residue accuracy, Q3, reached 83.99%, while on the CB513 dataset, Q3 reached 85.58%. Meanwhile, on the CASP8 sequences, the results are superior to those produced by other methods, such as Psipred, Jpred, APSSP2 and BehairPred. These results confirm that our method has a strong generalization ability, and that it provides a model for the construction of other intelligent systems.     

Multivariate time series prediction of complex systems based on graph neural networks with location embedding graph structure learning

Graph convolutional neural networks (GNNs) have an excellent expression ability for complex systems. However, the smoothing hypothesis based GNNs have certain limitations for complex process industrial systems with high dynamics and noisy environment. In addition, it is difficult to obtain an accurate information about the interconnections of sensor networks in manufacturing systems, which brings challenges to the application of GNNs. This paper introduces a graph convolution filter with a serial alternating structure of low-pass filter and high-pass filter to alleviate the problem of node feature loss. Furthermore, we propose a simple and effective method to learn graph structure information during training. This method combines the advantages of graph structure learning based on metric method and direct optimization method. Finally, a spatiotemporal parallel feature extraction framework for multivariate time series prediction is constructed. Experiments are carried out on real industrial datasets, and the results demonstrate the effectiveness of the model.     

基于角色发现的动态信息网络结构演化分析

动态信息网络是当前复杂网络领域中极具挑战的新问题之一，对其动态的演化过程进行研究，有助于分析网络结构、理解网络特性、发现网络中潜在的信息及演化规律，具有重要的理论意义与应用价值.基于网络结构本身量化表示的复杂性以及网络演化时序、复杂、多变的挑战，使用角色来量化动态网络的结构，并对模型进行分析，给出了两种角色解释的方法；在角色发现的基础上，将动态网络结构预测问题转换为可以表示结构特征的角色预测问题，通过向量自回归的方法，以历史网络角色分布矩阵作为训练数据构建模型，预测未来时刻网络可能的角色分布情况，提出了基于潜在角色的动态网络结构预测方法LR-DNSP（latent role based dynamic network structure prediction）.该方法克服了已有基于转移矩阵方法忽略历史信息的不足，并且考虑了多个预测目标之间可能存在的相互关系.实验结果表明，提出的LR-DNSP方法具有更准确的预测效果.     

基于AFP的有向加权注意力流网络链路预测

个性化推荐系统在减轻信息超载、提供个性化服务和辅助用户决策等方面应用广泛,链路预测是个性化推荐的重要方法之一。传统启发式链路预测方法仅考虑网络的图结构特征,缺乏对显式特征和隐式特征信息的应用,且大多数方法基于无向无权网络。针对传统链路预测方法存在的不足,基于集体注意力流网络和R-GCN方法,提出了链路预测算法AFP,将注意力流网络中2节点间不同的边方向抽象为2种边关系类型,并引入注意力机制学习网络中的节点属性和边属性,还综合考虑了网络的图结构特征、显式特征和隐式特征,最后通过评分函数得到三元组成立与否的概率,将链路预测问题转化为一个二分类问题,预测节点间的边属于某个关系类型的可能性。实验结果表明,相比于GCN、GAT等6个基准算法,该算法在准确度、精度和召回率等多个评价指标上均有提升。     

Identification of a class of nonlinear state-space models using RPEtechniques

The RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator     

线性混杂系统优化控制的Monte Carlo统计预测方法

针对含扩散项的线性混杂切换系统优化控制问题, 为降低优化求解的计算复杂性, 提出了Monte Carlo统计预测方法. 首先通过数值求解技术把连续时间优化控制问题转化为离散时间的Markov决策过程问题; 然后在若干有限状态子空间内, 利用反射边界技术来求解相应子空间的最优控制策略; 最后根据最优控制策略的结构特性, 采用统计预测方法来预测出整个状态空间的最优控制策略. 该方法能有效降低求解涉及大状态空间及多维变量的线性混杂切换系统优化控制的计算复杂性, 文末的仿真结果验证了方法的有效性.     

动态信息网络中基于角色的结构演化与预测

动态信息网络是当前复杂网络领域一个极具挑战的新问题,其动态的演化过程具有时序、复杂、多变的特点.结构是网络最基本的特征,也是进行网络建模和分析的基础,研究网络结构的演化过程对全面认识复杂系统的行为倾向具有重要意义.使用“角色”来量化动态网络的结构,得到动态网络的角色模型,应用并改进多类标分类问题的“问题转换”思想,将动态网络的角色预测问题视为多目标回归问题,以历史网络数据作为训练数据构建模型,预测未来时刻网络可能的角色分布情况,提出基于多目标回归思想的动态网络角色预测方法MTR-RP.该方法不仅克服了基于转移矩阵方法忽略时间因素的不足,并且考虑了多个预测目标之间可能存在的依赖关系,实验结果表明,本文提出的MTR-RP方法具有更准确且更稳定的预测效果.     

随机环境下的因果推理方法

文中给出了系统结构深层知识的因果解释，提出了随机环境下平均结构建模方法，建立了一种经济市场分析模型，并进行了因果顺序分析和因果效用分析。     

Swarm intelligence algorithm for interconnect model order reduction with sub-block structure preserving

In this paper, we propose a generalised sub-block structure preservation interconnect model order reduction (MOR) technique based on the swarm intelligence method, that is, particle swarm optimisation (PSO). The swarm intelligence-based structure preservation MOR can be used for a standard model as a criterion for different structure preservation interconnect MOR methods. In the proposed technique, the PSO method is used for predicting the unknown elements of structure-preserving reduced-order modelling of interconnect circuits. The prediction is based on minimising the difference of transform function between the original full-order and desired reduced-order systems maintaining the full-order structure in the reduced-order model. The proposed swarm-intelligence-based structure-preserving MOR method is compared with published work on structure preservation MOR SPRIM techniques. Simulation and synthesis results verify the accuracy and validity of the new structure-preserving MOR technique.