Multi-granularity and metric spatial reasoning

Composition reasoning is a basic reasoning task in qualitative spatial reasoning (QSR). It is an important qualitative method for robot navigation, node localization in wireless sensor networks and other fields. The previous composition reasoning works dedicated in single granularity framework. Multi-granularity spatial relation is not rare in real world, and some qualitative spatial relation models are multi-granularity models, such as RCC, STAR_m, CDC_m and OPRA_m. Although multi-granularity composition reasoning is very useful in many applications, it has not been systematically studied before. A special case of multi-granularity composition reasoning, referred to as metric spatial reasoning, is also discussed here. The general frameworks and basic theories for multi-granularity and metric spatial reasoning are put forward here. Furthermore, we redefine the spatial relation models for distance, topology and direction under the proposed multi-granularity and metric frameworks. We add metric representation for the OPRA_m. The multi-granularity and metric reasoning tasks are studied for these four models for the first time. Finally we perform some experiments on OPRA_m with encouraging results to verify our theories. Multi-granularity and metric spatial reasoning tasks are new problems in QSR and quite different from the previous works. Our works can be potentially applied in robot navigation, wireless sensor networks and other applications.     

Mining qualitative patterns in spatial cluster analysis

Clustering is an important concept formation process within AI. It detects a set of objects with similar characteristics. These similar aggregated objects represent interesting concepts and categories. As clustering becomes more mature, post-clustering activities that reason about clusters need a great attention. Numerical quantitative information about clusters is not as intuitive as qualitative one for human analysis, and there is a great demand for an intelligent qualitative cluster reasoning technique in data-rich environments. This article introduces a qualitative cluster reasoning framework that reasons about clusters. Experimental results demonstrate that our proposed qualitative cluster reasoning reveals interesting cluster structures and rich cluster relations.     

一种加权模糊推理网络模型及其应用

提出了一种加权模糊推理网络的结构模型和学习算法,该网络的基本信息处理单元为模糊推理神经元,融合了模糊逻辑能够较完整地表达领域规则和先验知识,以及神经网络自适应环境的优点。根据模糊推理规则的量化表示形式和微分方程数值解的动力学思想推导出了该网络模型的学习算法。该算法具有稳定、收敛速度快,且能较好地避免网络学习陷入局部极值点。以油田生产复杂水淹层识别问题为例,验证了模型和算法的有效性。     

A method of qualitative spatial envisionment with applications to planar mechanism configuration modeling and planning

This paper describes a method for qualitatively representing and reasoning about spatial configurations ofplanar mechanisms. The method has direct relevance to, and implications for,computer-aided mechanism design androbotics. In particular, it can be used to solve spatial configuration problems where exact geometric knowledge is not available, and to provide guidance for the application of quantitative configuration modeling and planning methods. In this paper, two applications of this method are demonstrated. The first application is concerned with inferring the instantaneous configurations and coupler curves inone-degree-of-freedom planar linkages. The second application deals with planning collision-free paths foropen-chain planar mechanisms moving among static obstacles.     

QML-AiNet: An immune network approach to learning qualitative differential equation models

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.     

加权模糊推理网络及在水淹层识别中的应用

提出了一种加权模糊推理网络的结构模型和学习算法,该网络的基本信息处理单元为模糊推理神经元,融合了模糊逻辑能够较完整的表达领域规则和先验知识以及神经网络自适应环境的优点。根据模糊推理规则的量化表示形式和微分方程数值解的动力学思想推导出网络一种新的学习算法。该算法具有稳定,收敛速度快,且能较好避免网络学习陷入局部极值点。以油田生产复杂水淹层识别问题为例,验证了模型和算法的有效性。     

HSOP: A neural network paradigm and its applications

This paper presents a neural network paradigm, case studies on its applications and performance. We called this paradigm the Hybrid Sum-of-Products (HSOP), and it is an improvement on both sum-of-products and backpropagation paradigms. In the HSOP architecture, the lowest layer (input layer) is connected to the layer above in the standard backpropagation manner. Subsequent layers are connected in the sum-of-products manner. The learning rates required for the HSOP are similar to those required for sum-of-products, with slight differences. Since input units do not have defined error, the fact that they are connected differently has no consequences on the calculations of the error and values for the rest of the units. The proposed paradigm was applied to two classification problems: computer user identification and characterisation of ultrasonic transducers. In both cases, the HSOP showed faster learning than backpropagation and sum-of-products without a significant computational penalty, since the number of its weights is comparable to backpropagation. The classification accuracy of HSOP when applied to the two applications is better than the traditional sum-of-products, and is comparable to that of the backpropagation.     

A new modal logic for reasoning about space: spatial propositional neighborhood logic

It is widely accepted that spatial reasoning plays a central role in artificial intelligence, for it has a wide variety of potential applications, e.g., in robotics, geographical information systems, and medical analysis and diagnosis. While spatial reasoning has been extensively studied at the algebraic level, modal logics for spatial reasoning have received less attention in the literature. In this paper we propose a new modal logic, called spatial propositional neighborhood logic (SpPNL for short) for spatial reasoning through directional relations. We study the expressive power of SpPNL, we show that it is able to express meaningful spatial statements, we prove a representation theorem for abstract spatial frames, and we devise a (non-terminating) sound and complete tableaux-based deduction system for it. Finally, we compare SpPNL with the well-known algebraic spatial reasoning system called rectangle algebra.      

A model of argumentation and its application to legal reasoning

We present a computational model of dialectical argumentation that could serve as a basis for legal reasoning. The legal domain is an instance of a domain in which knowledge is incomplete, uncertain, and inconsistent. Argumentation is well suited for reasoning in such weak theory domains. We model argument both as information structure, i.e., argument units connecting claims with supporting data, and as dialectical process, i.e., an alternating series of moves by opposing sides. Our model includes burden of proof as a key element, indicating what level of support must be achieved by one side to win the argument. Burden of proof acts as move filter, turntaking mechanism, and termination criterion, eventually determining the winner of an argument. Our model has been implemented in a computer program. We demonstrate the model by considering program output for two examples previously discussed in the artificial intelligence and legal reasoning literature.     

Probabilistic reasoning in an augmented fact-proposition space and its applications

A simple and versatile probabilistic reasoning scheme is presented. Based on an augmentation of a multi-dimensional inference space indexed by a Cartesian product of the fact and proposition sets, the scheme simplifies the processes involved in the representation and computation of a probabilistic reasoning system. In the augmented space, a set of auxiliary fields is utilized in addition to the fact-proposition relations to manipulate the uncertainty and incompleteness of the information presented. The scheme enhances the functionality of a probabilistic reasoning and facilitates the building of practical reasoning systems. The utilization of the augmented space in reasoning is illustrated by two problems in computer-vision applications.     

EMD-BP神经网络预测模型及应用

时间序列分析是根据客观事物的连续性和规律性推测未来发展趋势的预测方法,分析时设法过滤除去不规则变动,突出反映趋势性和周期性变动。为了提高预测精度,构建了EMD-BP神经网络预测模型,利用Hilbert-Huang变换中的经验模态分解将时间序列分解为有限个本征模函数,重构后进行BP神经网络预测。通过对中国石化的股票资料进行实验仿真,表明该模型降低了被预测数据的非平稳性,其精度比直接用神经网络预测有较明显的提高。     

一个邮政运输网络优化模型及解法

通过对我国邮政运输系统的现状和特点的研究,构造了邮区内邮政运输网络的优化模型,用分组编码遗传算法进行求解.在分组编码遗传算法中加入禁忌思想,构造了适合的适应度值函数,通过实例验证了该解法的优越性.     

A polynomial fuzzy neural network for modeling and control

This paper introduces a novel neurofuzzy system based on polynomial fuzzy neural network (PFNN) architecture. A PFNN consists of a set of if-then rules with appropriate membership functions (MFs) whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection is defined to overcome the overfitting problem in the modeling procedure. For a performance assessment of the PFNN inference system, two well-known problems are employed for a comparison with other methods. The results of these comparisons show that the PFNN inference system out-performs the other methods and exhibits robustness characteristics. This work was presented in part at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7–31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.     

Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network

This work is dedicated to develop an algorithm for the visual quality recognition of nonwoven materials, in which image analysis and neural network are involved in feature extraction and pattern recognition stage, respectively. During the feature extraction stage, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. Then the wavelet coefficients in each subband are independently modeled by the generalized Gaussian density (GGD) model to calculate the scale and shape parameters with maximum likelihood (ML) estimator as texture features. While for the recognition stage, the robust Bayesian neural network is employed to classify the 625 nonwoven samples into five visual quality grades, i.e., 125 samples for each grade. Finally, we carry out the outlier detection of the training set using the outlier probability and select the most suitable model structure and parameters from 40 Bayesian neural networks using the Occam's razor. When 18 relevant textural features are extracted for each sample based on the GGD model, the average recognition accuracy of the test set arranges from 88% to 98.4% according to the different number of the hidden neurons in the Bayesian neural network.     

The mean-shift outlier model in general weighted regression and its applications

Consider the general weighted linear regression model y=Xβ+, where E()=0, Cov()=Vσ², σ² is an unknown positive scalar, and V is a symmetric positive-definite matrix not necessary diagonal. Two models, the mean-shift outlier model and the case-deletion model, can be employed to develop multiple case-deletion diagnostics for the linear model. The multiple case-deletion diagnostics are obtained via the mean-shift outlier model in this article and are shown to be equivalent to the deletion diagnostics via the case deletion model obtained by Preisser and Qaqish (1996, Biometrika, 83, 551–562). In addition, computing the multiple case-deletion diagnostics obtained via the mean-shift outlier model is faster than computing the one based on the more commonly used case-deletion model in some situations. Applications of the multiple deletion diagnostics developed from the mean-shift outlier model are also given for regression analysis with the likelihood function available and regression analysis based on generalized estimating equations. These applications include survival models and the generalized estimating equations of Liang and Zeger (1986, Biometrika, 73, 13–22). Several numerical experiments as well as a real example are given as illustrations.     

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

Integrated evidential reasoning approach in the presence of cardinal and ordinal preferences and its applications in software selection

A combination of cardinal and ordinal preferences in multiple-attribute decision making (MADM) demonstrates more reliability and flexibility compared with sole cardinal or ordinal preferences derived from a decision maker. This situation occurs particularly when the knowledge and experience of the decision maker, as well as the data regarding specific alternatives on certain attributes, are insufficient or incomplete. This paper proposes an integrated evidential reasoning (IER) approach to analyze uncertain MADM problems in the presence of cardinal and ordinal preferences. The decision maker provides complete or incomplete cardinal and ordinal preferences of each alternative on each attribute. Ordinal preferences are expressed as unknown distributed assessment vectors and integrated with cardinal preferences to form aggregated preferences of alternatives. Three optimization models considering cardinal and ordinal preferences are constructed to determine the minimum and maximum minimal satisfaction of alternatives, simultaneous maximum minimal satisfaction of alternatives, and simultaneous minimum minimal satisfaction of alternatives. The minimax regret rule, the maximax rule, and the maximin rule are employed respectively in the three models to generate three kinds of value functions of alternatives, which are aggregated to find solutions. The attribute weights in the three models can be precise or imprecise (i.e., characterized by six types of constraints). The IER approach is used to select the optimum software for product lifecycle management of a famous Chinese automobile manufacturing enterprise.