CLASSICAL AND NONCLASSICAL REPRESENTATIONS IN PHYSICS II: QUANTUM MECHANICS

The conceptual and formal structure of quantum mechanics is analyzed from the point of view of the dynamics of distinctions, occurring during the observation process. The Hilbert space formalism is simplified with the help of the concept of closure: Closure of an eigenstate under an operator is generalized to the linear closure of a subset of slates, and this is further simplified to orthogonal closure, meaning that a set of states can be distinguished by a single observation. Quantum states can be seen as (overlapping) subsets of unobservable infrastates, with the transition probability between two states proportional to the number of infra-stales they have in common. This makes it possible to reconstruct the superposition principle. An analysis of the observation process leads to the interpretation of closed sets of infrastates as attractors of the dynamics induced by the interaction with the observation apparatus. This interaction is always partially undeterminate, because of the unobservable microstate of the apparatus.     

FEELING DECISION SYSTEMS AND QUANTUM MECHANICS

From the viewpoint that the decision based on feeling has a structure similar to that of a quantum measurement process, we present an axiomatic system of probabilistic behaviors, by which we can liberate quantum mechanics from microscopic physics and attain a wide range of applicability of quantum mechanics-type theories, in particular lo human or biological systems. The key concept of this system is the feeling of the object system, which has no relation to the consciousness of an observer in the conventional measurement theory but corresponds directly to the wave function. The probabilistic and logical structures derived from the axioms are discussed. Several examples are taken from classical, quantum, and other models.     

SOME NEW ASPECT OF LYAPUNOV TYPE THEOREMS FOR STOCHASTIC DIFFERENCE EQUATIONS WITH CONTINUOUS TIME

Some new Lyapunov type theorems for stochastic difference equations with continuous time are proven. It is shown that these theorems simplify an application of Lyapunov functionals construction method.     

扩充内存在长时间高速数据采集中的应用

本文讨论了ＩＢＭ－ＰＣ微机扩充内存的访问方法，编制了Ｍｉｃｒｏｓｏｆｔ Ｃ语言直接访问扩充内存的汇编语言接口函数，并将其应用于某型液体火箭发动机振动信号分析系统数据采集。     

STOCHASTIC STABILIZATION AND H∞ CONTROL FOR DISCRETE JUMPING SYSTEMS WITH TIME DELAYS

In this paper, robust stochastic stabilization and H_∞ control for a class of uncertain discrete‐time linear systems with Markovian jumping parameters are considered. Based on a new bounded real lemma derived upon an inequality recently proposed, a new iterative state‐feedback controller design procedure for discrete time‐delay systems is presented. Sufficient conditions for stochastic stabilization are derived in the form of linear matrix inequalities (LMIs) based on an equivalent model transformation, and the corresponding H_∞ control law is given. Finally, numerical examples are given to illustrate the solvability of the problems and effectiveness of the results.     

实时内存数据库的数据交换策略及评价

实时数据库系统（ＲＴＤＢＳ）的高性能要求以内存数据库（ＭＭＤＢ）做底层支持内存数据库事务在运行过程中没有内外存数据Ｉ／Ｏ,这就要求对事务的执行进行预分析处理并在此基础上合理进行的内外存数据交换,文中以正在开发的一个实时数据库系统为原型,对数据库的内外存数据交换的策略、方法、进行了探讨,并提出了一咱评价交换策略的标准。     

Few算子和多项式时间概率算法PP的能力

本文提出Ｆｅｗ算子并研究其决定的复杂性类，得到了复杂性类ＰｔｔＣ的新刻划．利用此刻划讨论了多项式时间概率复杂性类ＰＰ的能力．     

随机二元神经网络的学习与泛化

本文提出了一种采用随机加权联接的新型神经网络模型,该模型具有清晰可分析的神经元激活函数,内部数据表示为随机二进制序列形式,硬件实现十分简洁高效.本文在三个不同层次对前向型网络的学习算法和系统仿真进行了深入的讨论,其中最底层的学习对应于硬件实时在线学习.另外,本文还对系统的泛化性能作了全面的分析,给出了VC维和学习样本量的明确结果.通过对三个用于标准测试的Monk问题和数字手写体识别问题的检测,获得了很好的实验结果.     

一种实时内存数据库组织与管理方法

文中首先给出了一种合理的内存数据库定义，然后针对实时的事务和数据特征以及各种存储介质的特性，提出了一种有效的实时内存数据库组织方法，并着重讨论了相应的数据安置策略和内外存数据交换策略．     

CASE-BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES

This paper discusses the role and integration of knowledge discovery (KD) in case-based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case-specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co-occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica-tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co-occurrence between certain sequences and classes.     

实现记忆检索和回忆中漫游的自传体记忆模型

为了捕捉自传体记忆和受开发类人智能的启发,给出一种可实现记忆检索和回忆中漫游的自传体记忆模型.该模型是一个3层网络结构,其底层将由5W1H构成的特定事件知识进行编码,提供检索线索;中间层通过关联特定事件知识对事件进行编码;顶层通过关联相关事件对事件集进行编码.模型按照自下而上的记忆搜索过程,可以分别在中间层和顶层识别相...     

供应链牛鞭效应的随机控制

牛鞭效应是一个衡量供应链管理运作是否良好的重要指标参数．本文采用随机控制 理论方法，针对在文[1]基础上建立的具有多分销中心的供应链动态库存模型，以供应链 系统中的订货作为控制变量抑制牛鞭效应．同时以一个石油分销系统为对象进行了供应链牛 鞭效应的随机控制仿真实验，实验表明牛鞭效应受到了抑制．     

CONTROL TASKS WITH. EXTENDED DISTANCES BETWEEN OPERATOR AND DISPLAY

Abstract

An account is given of an investigation into the performance of three control tusks with the operator at a considerable distance from the display. Subjects had to acquire a target at ranges of from 30-60 yards. It was found that, within these limits, the physical size of the target was very much more important than its angular (apparent) size. This was true both with full view of the surroundings of the target and with restricted viewing conditions when cues to visual size-constancy wore greatly reduced.     

非线性时变随机系统状态及参数的实时联合估计

在文[1]中,我们给出了一种用于一类非线性时变随机系统的带次优渐消因子的扩展卡尔曼滤波器,可以估计出快速变化的系统状态.本文推广了文[1]的结果,使其可处理一般的非线性测量.同时,给出了一种状态及参数的联合估计方法.所做大量仿真研究表明,本文方法具有良好的实时性及动态跟踪性.     

基于多尺度分析和神经网络的时间序列预报

本文提出一种由小波变换和神经网络相结合,进行时间序列预报的新方法。其中,小波作为滤波部分对原始序列进行多尺度分解,产生更容易建模和预测的子序列,再把上述子序列作为神经网络的输入进行时域预报。该方法考虑原时间序列的频率特性,采用不同的神经网络进行预报。     

SPACE AND TIME IN COMPUTATION AND DISCRETE PHYSICS

A step can be regarded as an elementary ordering of two objects (or operators). A step is a distinction combined with an action that crosses the boundary of that distinction. The elementary step can be seen as a reference, as a division of space or as a tick of a clock. By looking at the structure of a step, we provide a context that unifies specific aspects of special relativity, Laws of Form, topology, discrete physics and logic design.     

商业销售分析中具有时间约束的交易规则模型及实现

本文分析了在商业销售分析中常见的具有时间约束的交易规则,通过将其转换为一类特殊的关联规则,建立了该类规则的模型,最后对FP－Growth算法进行了扩展,实现了该模型。     

向后滚动检查点技术在虚存中的应用

本文介绍了容错重复恢复技术中的向后滚动检查点算法，并针对减少系统开销的问题，提出了在虚拟存贮器中的一种实现方案。     

随机模型的频域分析及辨识

本文证明了时间域内的平稳相关序列经离散傅里叶变换之后,听得到的是频域内的正交随机序列。因此,当系统的激励信号为确定性信号,而输出信号被平稳相关随机噪声所污染时,根据输入信号、输出信号的采样序列所得到的频率响应函数也是正交随机序列。利用方差的倒数加权进行最小二乘估计,可以得到系统连续模型参数的有效估计值。     

AI和CIPS在化工过程中的应用

作者从深度和广度两方面探讨了信息技术应用于化工过程的新进展。在深度上主要探讨了人工智能，特别是基于知识的系统和人工神经网络在化工过程中的应用概况２。在广度上主要探讨了化工生产向工业综合自动化方向发展的趋势是具体理论与技术内容。