A polynomial space construction of tree-like models for logics with local chains of modal connectives

The LA-logics (“logics with Local Agreement”) are polymodal logics defined semantically such that at any world of a model, the sets of successors for the different accessibility relations can be linearly ordered and the accessibility relations are equivalence relations. In a previous work, we have shown that every LA-logic defined with a finite set of modal indices has an NP-complete satisfiability problem. In this paper, we introduce a class of LA-logics with a countably infinite set of modal indices and we show that the satisfiability problem is PSPACE-complete for every logic of such a class. The upper bound is shown by exhibiting a tree structure of the models. This allows us to establish a surprising correspondence between the modal depth of formulae and the number of occurrences of distinct modal connectives. More importantly, as a consequence, we can show the PSPACE-completeness of Gargov's logic DALLA and Nakamura's logic LGM restricted to modal indices that are rational numbers, for which the computational complexity characterization has been open until now. These logics are known to belong to the class of information logics and fuzzy modal logics, respectively.     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

Dynamical Responses of Chaotic Memory Dynamics to Weak Input in a Recurrent Neural Network Model

Chaotic dynamics in a recurrent neural network model, in which limit cycle memory attractors are stored, is investigated by means of numerical methods. In particular, we focus on quick and sensitive response characteristics of chaotic memory dynamics to external input, which consists of part of an embedded memory attractor. We have calculated the correlation functions between the firing activities of neurons to understand the dynamical mechanisms of rapid responses. The results of the latter calculation show that quite strong correlations occur very quickly between almost all neurons within 1 ~ 2 updating steps after applying a partial input. They suggest that the existence of dynamical correlations or, in other words, transient correlations in chaos, play a very important role in quick and/or sensitive responses.     

Discovery of a perceptual distance function for measuring image similarity

For more than a decade, researchers have actively explored the area of image/video analysis and retrieval. Yet one fundamental problem remains largely unsolved: how to measure perceptual similarity between two objects. For this purpose, most researchers employ a Minkowski-type metric. Unfortunately, the Minkowski metric does not reliably find similarities in objects that are obviously alike. Through mining a large set of visual data, our team has discovered a perceptual distance function. We call the discovered function the dynamic partial function (DPF). When we empirically compare DPF to Minkowski-type distance functions in image retrieval and in video shot-transition detection using our image features, DPF performs significantly better. The effectiveness of DPF can be explained by similarity theories in cognitive psychology.     

基于神经网络的非线性模型预测控制

该文提出了一种基于神经网络的模型预测控制结构 ,并使用一个新的随机搜索优化算法来求解预测控制律 ,计算机仿真证明了所设计的控制算法的正确性和有效性     

哈尔滨公路客运总站联网设计与实现

介绍哈尔滨公路客运总站网络系统的硬件组成、软件设计及网络安全等实现技术。     

量子查找算法的发展与前瞻

量子查找算法是一种利用波的特性进行查找的新方法，它以量子位作为描述问题 的基本信息单位，为 NP-完全问题的解决提供了一种有效的途径。量子查找算法的主要特 点 是查找的高度并行性、非结构化查找和巨大的信息存储容量。该文介绍了量子查找的基 本思 想；综述了量子查找的典型实例及其广泛应用；分析了量子查找算法的特点及其与传 统算法 的关系；指出了量子计算目前存在的问题；最后对量子计算的发展前景进行展望。     

数字地图局部自动生成与半自动编辑

数字地图的局部自动生成与半自动编辑系统由道路编辑模块和房屋编辑模块构成。文章详细描述了各模块中的半自动提取功能的算法，其中道路提取采用了滚球法，房屋提取采用的是基于几何关系的算法。所有半自动提取算法都在图形的基础上操作的，能保证有较好的通用性，不仅可用于纸面地图生成数字地图的开发过程，而且适用于从航空图像开发数字地图等用途。数字地图半自动编辑器已可作为一个独立的系统，在人机交互状态下自动／半自动生成数字道路地图和数字房屋地图。     

冒泡择优遗传算法

为快速地寻求复杂多峰函数的全局极值点，提出一种冒泡择优遗传算法。它以冒泡的形式让每一代种群中的最优的个体参加列队竞争，以成为局部最优或全局最优。对于达到局部极值的个体，进行湮灭操作，重新进行进化与列队竞争。该算法能自动保持种群多样性且易于实现。实验结果表明，该算法对于求解多峰函数优化的问题十分有效，通常都能找到全部全局最优解。     

基于削减搜索分支的快速模板匹配算法

提出了一种在完全搜索中寻找最优匹配点的模板匹配算法。它首先为图像建立一种类似金字塔的特殊层次结构。利用该结构的特点，削减匹配中无用的搜索分支，以达到提高处理效率的目的。通过该算法找到了完全搜索的最优匹配点，实验结果证明了它可以大大提高处理的效率。