动态系统的模糊关系建模算法及实现

模糊集理论是一种研究复杂系统的有效的定性方法，本文研究在动态系统建模中使用模糊集和语言变量的方法。文章首先给出了基于模糊关系的动态系统模型表达形式，进而导出了一种利用牛顿梯度下降的模糊系统建模算法。并就一个简单的例子验证了上述算法的有效性。     

基于区间2型T-S模糊系统的自适应逆控制

复杂非线性系统存在强非线性和不确定性等问题,其建模与控制一直是个极具挑战的工作。自适应逆控制是一种有效的非线性系统控制方法,已经得到广泛的研究;2型模糊系统采用2型模糊集,相比于1型模糊系统,其能够提供更大的自由度,不确定性及非线性处理能力更强,能够采用较少的规则数取得较高的建模与控制精度。因此,本文将2型模糊系统理论与自适应逆控制相结合,提出了一种基于区间2型T-S模糊系统的自适应逆控制方法,实现对复杂非线性系统的有效建模与控制。首先通过离线输出输入数据映射得到非线性系统的离线2型模糊逆模型,然后将该离线区间2型模糊逆模型作为初始控制器,与被控对象串联,进行在线控制,并采用最小均方差(Least Mean Square,LMS)滤波算法在线修正2型模糊逆模型的结论参数,通过数字复制,更新逆模型控制器的参数。最后将该方法应用于两个仿真实例,结果表明本文方法控制精度高,不确定性处理能力强。     

基于模糊与神经网络技术的复杂非线性跟踪控制

针对一类具有不确定性、多重时延和状态未知的复杂非线性系统,把模糊T-S模型和RBF神经网络结合起来,提出了一种基于观测器的跟踪控制方案.首先,应用模糊T-S模型对非线性系统建模,设计观测器用来观测系统状态,并由线性矩阵不等式得到模糊模型的控制律;其次,构建了自适应RBF神经网络,应用自适应RBF神经网络作为补偿器来补偿建模误差和不确定非线性部分.证明了闭环系统满足期望的跟踪性能.示例仿真结果表明了该方案的有效性.     

多元模糊回归预测模型及其应用

论述多元模糊回归预测模糊的建模方法,探讨该预测模型在第二代玉米螟百株卵量各动态上的应用,研究结果表明,该预测模型为害害虫群动态的中长期预测预报提供了一种新的研究方法,是一种优良的模型。     

不确定对象的自学习模糊神经网络控制方法

本文对具有不确定性控制对象提出了一种自学习模糊神经网络控制方法，模糊控制器采用误差，误差变化及误差加速度的加权和解析描述形式，利用人工神经网络直接对过程的建模，实现对模糊加权因子的自学习优化调整。将上述方法用于焊接熔池动态过程控制实实验，结果表明本文提出的自学习模糊神经网络控制方案有效。     

输入变量个数对T-S模糊建模精度的影响

输入变量个数会对模糊建模精度产生影响.对于一个实际的复杂系统,可测的或者需要考虑的输入变量非常多.是不是考虑的影响因素越多,即模糊系统的输入变量越多,则辨识的效果就越好呢?本文基于T-S模糊模型,分别采用对称三角形模糊划分和网格对角线法以及模糊聚类划分提取模糊规则,对Box-Jenkins煤气炉数据和Mackey-Glass混沌时间序列进行建模,得到了模糊模型训练性能指标和检验性能指标随输入变量个数增加时的变化趋势曲线,并给出了结论.     

基于模糊变换的模糊系统及HX方程

本文研究基于模糊变换的模糊系统的构造方法和模糊推理建模法问题。首先,给出了利用单入-单出模糊系统和模糊变换构造双输入-单输出模糊系统的方法,指出这种模糊系统具有泛逼近性,并给出了该模糊系统具有泛逼近性的充分条件。其次,将该模糊系统应用到模糊推理建模法中,得到了一种新的HX方程,泛逼近性定理说明:该HX方程对原系统具有很好的泛逼近性。最后,将得到的新的HX方程应用到自治Lienard系统中,得到了不含一阶导数项的简化HX方程。简化的HX方程将原先逐片求解(m-1)(n-1)个方程,简化为逐片求解m-1个方程,从而降低了计算复杂度。仿真实验说明了新HX方程的有效性。     

模糊控制系统的建模

提出一种基于模糊推理的关于控制系统的建模方法,称之为模糊推理建模法, 它可以作为不同于熟知的机理建模法和系统辨识建模法的第3种建模方法. 该方法根据模糊逻辑系统的插值机理将关于被控对象的模糊推理规则库转换为一类变系数非线性微分方程(组), 从而得到控制系统的数学模型;这样便解决了在模糊控制系统中被控对象难于建模的问题.     

分层模糊控制器在四足步行机器人中的应用

本文针对四足步行机器人模糊控制器规则庞大，逻辑复杂的问题，提出了一种分层模糊控制器的设计方法。该方法不依赖被控对象的数学模型，将状态变量分层以降低多变量系统的设计复杂性，仿真和实验结果显示了该方法的有效性。     

我国上市公司被特别处理的组合预测模型研究

针对组合预测比单项预测具有更高的预测精度，本提出了一种基于模糊神经网络的上市公司被ST的非线性组合建模与预测新方法，并给出了相应的混合学习算法。通过与多元线性回归模型、Fisher模型和Logistc回归模型的预测结果对比表明，该方法具有预测精度高，学习与泛化能力强，适应性广的优点。     

A General Framework for Mining Massive Data Streams

In many domains, data now arrive faster than we are able to mine it. To avoid wasting these data, we must switch from the traditional “one-shot” data mining approach to systems that are able to mine continuous, high-volume, open-ended data streams as they arrive. In this article we identify some desiderata for such systems, and outline our framework for realizing them. A key property of our approach is that it minimizes the time required to build a model on a stream while guaranteeing (as long as the data are iid) that the model learned is effectively indistinguishable from the one that would be obtained using infinite data. Using this framework, we have successfully adapted several learning algorithms to massive data streams, including decision tree induction, Bayesian network learning, k-means clustering, and the EM algorithm for mixtures of Gaussians. These algorithms are able to process on the order of billions of examples per day using off-the-shelf hardware. Building on this, we are currently developing software primitives for scaling arbitrary learning algorithms to massive data streams with minimal effort.     

Modelling non-stationary dynamic gene regulatory processes with the BGM model

Recently, a Bayesian network model for inferring non-stationary regulatory processes from gene expression time series has been proposed. The Bayesian Gaussian Mixture (BGM) Bayesian network model divides the data into disjunct compartments (data subsets) by a free allocation model, and infers network structures, which are kept fixed for all compartments. Fixing the network structure allows for some information sharing among compartments, and each compartment is modelled separately and independently with the Gaussian BGe scoring metric for Bayesian networks. The BGM model can equally be applied to both static (steady-state) and dynamic (time series) gene expression data. However, it is this flexibility that renders its application to time series data suboptimal. To improve the performance of the BGM model on time series data we propose a revised approach in which the free allocation of data points is replaced by a changepoint process so as to take the temporal structure into account. The practical inference follows the Bayesian paradigm and approximately samples the network, the number of compartments and the changepoint locations from the posterior distribution with Markov chain Monte Carlo (MCMC). Our empirical results show that the proposed modification leads to a more efficient inference tool for analysing gene expression time series.     

Learning management knowledge for manufacturing systems in the early stages using time series data

Acquiring knowledge in manufacturing systems in the early stages always has a challenging task due to the lack of sufficient data. This makes it hard for the derived management model to reach a reliable and stable level. Li and Lin (2006) developed a useful method that can deal with the problem of knowledge acquisition based on a small data set. However, this method assumes all data are collected at the same time, since they treat the data set as a source (from one population) of a priori knowledge for learning. In fact, instead of being a random data set, these collected data can be time dependent, that is, they tend to be a sequence of observations, occurring at different times. The consideration of this dependence property in the data will benefit the knowledge acquisition in the early stages by expanding the learning model from an independent model to a dependent model. This research expanded the intervalized kernel density estimator (IKDE) presented in Li and Lin (2006) to a more general form to improve the learning model in the early stages. The general model, called GIKDE, joints the concepts of time series and stochastic processes in order to deal with both independent and dependent data sets. The Virtual Sample Generation process based on GIKDE was also developed to produce extra information for expediting the learning. Results obtained from the application of the model to a control charts data, using a back-propagation neural network as the learning tool, show that this unique approach is an effective method of knowledge acquisition for a manufacturing system in the early stages.     

A Gaussian Mixed Model for Learning Discrete Bayesian Networks

In this paper, we address the problem of learning discrete Bayesian networks from noisy data. A graphical model based on a mixture of Gaussian distributions with categorical mixing structure coming from a discrete Bayesian network is considered. The network learning is formulated as a maximum likelihood estimation problem and performed by employing an EM algorithm. The proposed approach is relevant to a variety of statistical problems for which Bayesian network models are suitable—from simple regression analysis to learning gene/protein regulatory networks from microarray data.     

Inferring missing links in partially observed social networks

Determining the pattern of links within a large social network is often problematic due to the labour-intensive nature of the data collection and analysis process. With constrained data collection capabilities it is often only possible to either make detailed observations of a limited number of individuals in the network, or to make fewer observations of a larger number of people. Previously we have shown how detailed observation of a small network can be used, which infer where in the network previously unconnected individuals are likely to fit, thereby attempting to predict network growth as new people are considered for inclusion. Here, by contrast, we show how social network topology can be inferred following a limited observation of a large network. Essentially the issue is one of inferring the presence of links that are missed during a constrained data collection campaign on the network. It is particularly difficult to infer network structures for those organizations that actively seek to remain covert and undetected. However, it is often very useful to know if two given individuals are likely to be connected even though limited surveillance effort yields no evidence of a link. Specifically, we show how a statistical inference technique can be used to successfully predict the existence of links that are missed during network sampling. The procedure is demonstrated using network data obtained from open source publications.     

Learning intransitive reciprocal relations with kernel methods

In different fields like decision making, psychology, game theory and biology, it has been observed that paired-comparison data like preference relations defined by humans and animals can be intransitive. Intransitive relations cannot be modeled with existing machine learning methods like ranking models, because these models exhibit strong transitivity properties. More specifically, in a stochastic context, where often the reciprocity property characterizes probabilistic relations such as choice probabilities, it has been formally shown that ranking models always satisfy the well-known strong stochastic transitivity property. Given this limitation of ranking models, we present a new kernel function that together with the regularized least-squares algorithm is capable of inferring intransitive reciprocal relations in problems where transitivity violations cannot be considered as noise. In this approach it is the kernel function that defines the transition from learning transitive to learning intransitive relations, and the Kronecker-product is introduced for representing the latter type of relations. In addition, we empirically demonstrate on two benchmark problems, one in game theory and one in theoretical biology, that our algorithm outperforms methods not capable of learning intransitive reciprocal relations.     

Emergence of order from chaos by cooperative non-biased learning between random bit-strings

Numerical experiments show that non-biased learning between families of independent and random bit-strings causes order. A parallel distributed learning between these bit-strings is performed by a neural network of the type pattern associator. The system allows emergence of some order in the learning matrix when a non-linear process is used, while a linear learning is unable to break the learning-matrix randomness. This neural network is in fact a complex and non-linear dynamical system, and consequently is able to self-organize order from chaos. It is also a model of collective proto-cognition that would describe biological evolution in species by cooperative learning. Our model suggests that the cause of evolution towards order in complex systems, can be just the learning process.     

A perspective on artificial intelligence: Learning to learn

The classical approach to the acquisition of knowledge in artificial intelligence has been to program the intelligence into the machine in the form of specific rules for the application of the knowledge: expert systems. Unfortunately, the amount of time and resources required to program an expert system with sufficient knowledge for non-trivial problem-solving is prohibitively large. An alternative approach is to allow the machine tolearn the rules based upon trial-and-error interaction with the environment, much as humans do. This will require endowing the machine with a sophisticated set of sensors for the perception of the external world, the ability to generate trial actions based upon this perceived information, and a dynamic evaluation policy to allow it to measure the effectiveness of its trial actions and modify its repertoire accordingly. The principles underlying this paradigm, known ascollective learning systems theory, have already been applied to sophisticated gaming problems, demonstrating robust learning and dynamic adaptivity.The fundamental building block of a collective learning system is thelearning cell, which may be embedded in a massively parallel, hierarchical data communications network. Such a network comprising 100 million learning cells will approach the intelligence capacity of the human cortex. In the not-too-distant future, it may be possible to build a race of robotic slaves to perform a wide variety of tasks in our culture. This goal, while irresistibly attractive, is most certainly fraught with severe social, political, moral, and economic difficulties.This paper was given as an invited talk on the 12th Symposium on Operations Research, University of Passau, September 1987.     

基于准ARX多层学习网络模型的非线性系统自适应控制

建立了准ARX多层学习网络预测模型,并用于非线性系统自适应控制问题.该模型的内核部分为一个改进的神经模糊网络(NFNs):一部分为三层非线性网络结构,采用自联想网络进行离线训练;另一部分为三层NFNs,采取在线调整.据此对参数进行分类,给出相应调整算法. 然后,基于模型宏观结构的优势给出控制器设计方案.仿真分析给出该建模方法的有效性.     

Modelling credit rating by fuzzy adaptive network

Human judgment plays an important role in the rating of enterprise financial conditions. The recently developed fuzzy adaptive network (FAN), which can handle systems whose behaviour is influenced by human judgment, appears to be ideally suited for the modelling of this credit rating problem. In this paper, FAN is used to model the credit rating of small financial enterprises. To illustrate the approach, the data of the credit rating problem is first represented by the use of fuzzy numbers. Then, the FAN network based on inference rules is constructed. And finally, the network is trained or learned by using the fuzzy number training data. The main advantages of the proposed network are the ability for linguistic representation, linguistic aggregation and the learning ability of the neural network.