Unsupervised adaptive neural-fuzzy inference system for solving differential equations

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential equations. The presented solution of differential equation consists of two parts; the first part satisfies the initial/boundary condition and has no adjustable parameter whereas the second part is an ANFIS which has no effect on initial/boundary conditions and its adjustable parameters are the weights of ANFIS. The algorithm is applied to solve differential equations and the results demonstrate its accuracy and convince us to use ANFIS in solving various differential equations.     

Gdel语言编译时类型处理技术研究

Gdel是一种发展中的逻辑程序设计语言,目前还没有开发出编译器。Gdel语言的类型系统独特于通常的逻辑程序设计语言,语义上已初步形成了相应的描述方法,建立了严格的理论基础。按照传统的方法,逻辑程序的运行机制和Gdel语言的多态性决定了一些变量的类型和语义只能在运行时才能确定,把这类变量的类型检查放到程序运行中处理,势必增加许多额外的计算。提出了一种处理Gdel语言类型系统的编译技术。针对语言的类型,对其相关语法进行适当修改,通过类型推断,把变量类型的检查工作提前到编译阶段完成,可以较好地处理其变量类型的语义问题,同时减少了运行时类型检查工作,从而提高程序的运行效率。     

Robust monitoring and fault reconstruction based on variational inference component analysis

Probabilistic models such as probabilistic principal component analysis (PPCA) have recently caught much attention in the process monitoring area. An important issue of the PPCA method is how to determine the dimensionality of the latent variable space. In the present paper, one of the most popular Bayesian type chemometric methods, Bayesian PCA (BPCA) is introduced for process monitoring purpose, which is based on the recent developed variational inference algorithm. In this monitoring framework, the effectiveness of each extracted latent variable can be well reflected by a hyperparameter, upon which the dimensionality of the latent variable space can be automatically determined. Meanwhile, for practical consideration, the developed BPCA-based monitoring method is robust to missing data and can also give satisfactory performance under limited data samples. Another contribution of this paper is due to the proposal of a new fault reconstruction method under the BPCA model structure. Two case studies are provided to evaluate the performance of the proposed method.     

基于模糊推理的多智能体强化学习

以电子市场智能定价问题为研究背景,提出基于模糊推理的多智能体强化学习算法(FI-MARL).在马尔科夫博弈学习框架下,将领域知识初始化为一个模糊规则集合,智能体基于模糊规则选择动作,并采用强化学习来强化模糊规则.该方法有效融合应用背景的领域知识,充分利用样本信息并降低学习空间维数,从而增强在线学习性能.在电子市场定价的...     

Analysis of an unobservable queue using arrival and departure times

Suppose one wants to assess the efficiency of a queueing system but is unable to observe directly its internal operations. This situation might arise if one works with a restricted set of historical data or because secrecy restricts access to the queueing facility. One might still be able to observe, from outside the system, the exact arrival and departure times of each customer. Using observations of arrival and departure times and knowledge of whether the service discipline is either first-come-first-served or last-come-first-served, one can exactly reconstruct the unobserved queue delays and service times of any sequence of arrivals during busy periods when the number of customers is greater than the number of servers. If the number of servers is also unknown, it too can be estimated. In this paper, we propose optimization models which determine the unknown number of servers.     

网页自动分类不确定性问题的贝叶斯网络解法

针对网页自动分类中存在的类边界模糊、语料不均匀等引起的分类不确定性问题,提出了贝叶斯网络自动分类融合模型和融合算法,该模型和算法基于网页上多种信息进行融合,并采用不同的与处理方法分别对多种信息进行处理,将处理后的信息输入到贝叶斯网络融合中心进行融合推理,得到最终的分类结果。同时,为了降低贝叶斯网络推理时间复杂度,提出了改进的贝叶斯网络图推理算法。实验结果表明,改进后的融合模型和融合算法能有效解决网页自动分类中的不确定性问题,并能提高网页自动分类的准确率和查全率。     

A probabilistic plan recognition algorithm based on plan tree grammars

We present the PHATT algorithm for plan recognition. Unlike previous approaches to plan recognition, PHATT is based on a model of plan execution. We show that this clarifies several difficult issues in plan recognition including the execution of multiple interleaved root goals, partially ordered plans, and failing to observe actions. We present the PHATT algorithm's theoretical basis, and an implementation based on tree structures. We also investigate the algorithm's complexity, both analytically and empirically. Finally, we present PHATT's integrated constraint reasoning for parametrized actions and temporal constraints.     

Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker entry temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.     

A stochastic graph grammar for compositional object representation and recognition

This paper illustrates a hierarchical generative model for representing and recognizing compositional object categories with large intra-category variance. In this model, objects are broken into their constituent parts and the variability of configurations and relationships between these parts are modeled by stochastic attribute graph grammars, which are embedded in an And-Or graph for each compositional object category. It combines the power of a stochastic context free grammar (SCFG) to express the variability of part configurations, and a Markov random field (MRF) to represent the pictorial spatial relationships between these parts. As a generative model, different object instances of a category can be realized as a traversal through the And-Or graph to arrive at a valid configuration (like a valid sentence in language, by analogy). The inference/recognition procedure is intimately tied to the structure of the model and follows a probabilistic formulation consisting of bottom-up detection steps for the parts, which in turn recursively activate the grammar rules for top-down verification and searches for missing parts. We present experiments comparing our results to state of art methods and demonstrate the potential of our proposed framework on compositional objects with cluttered backgrounds using training and testing data from the public Lotus Hill and Caltech datasets.     

Latent classification models for binary data

One of the simplest, and yet most consistently well-performing set of classifiers is the naïve Bayes models (a special class of Bayesian network models). However, these models rely on the (naïve) assumption that all the attributes used to describe an instance are conditionally independent given the class of that instance. To relax this independence assumption, we have in previous work proposed a family of models, called latent classification models (LCMs). LCMs are defined for continuous domains and generalize the naïve Bayes model by using latent variables to model class-conditional dependencies between the attributes. In addition to providing good classification accuracy, the LCM has several appealing properties, including a relatively small parameter space making it less susceptible to over-fitting. In this paper we take a first step towards generalizing LCMs to hybrid domains, by proposing an LCM for domains with binary attributes. We present algorithms for learning the proposed model, and we describe a variational approximation-based inference procedure. Finally, we empirically compare the accuracy of the proposed model to the accuracy of other classifiers for a number of different domains, including the problem of recognizing symbols in black and white images.