试论制约人工智能发展的逻辑问题

基于目前阻碍人工智能发展的各种因素 ,提出了人工智能发展中的逻辑问题 :非确定性推理、形象思维和常识问题。指出这些逻辑问题是制约人工智能发展的瓶颈。从理论上对这些问题进行了分析和探讨     

电弧炉电极系统双模控制策略研究及应用

针对交流电弧炉电极控制系统具有非线性时变、多变量、强耦合及存在随机干扰的特点,采用基于最近邻聚类方法的径向基函数(RBF)神经网络快速学习算法,通过实时在线辨识,建立电弧炉电极系统的精确逆模型并用于控制,实现了将具有强耦合特性的多变量输入/输出(MIMO)系统解耦成单个独立的伪线性对象,并提出一种基于RBF神经网络逆控制与比例微分(P/D)控制相结合的双模控制策略。应用结果证实了该控制策略具有快速适应对象和过程变化的能力及较强的鲁棒性。     

Identifying regular blocks in valued networks: A heuristic applied to the St. Marks carbon flow data,and international trade in cereal products

While the concept of regular equivalence is equally applicable to dichotomous as well as valued networks, the identification of regular blocks in regular blockmodels is somewhat problematic when dealing with valued networks. Applying the standard procedure for identifying ties in such blockmodels, a procedure perhaps most suited for dichotomous networks, does tend to generate block images and reduced graphs that differ from intuitive notions of such structures.     

关于移动自组网自安全证书管理模型

对目前建立移动自组网信任模型的各种主流方案进行了分类和评述,重点阐述了自安全证书管理模型各种方案的优缺点,并指出了今后的研究方向.     

发展用户驻地网与打破本地固话垄断

本文提出了一个发展用户驻地网的思路,着重论述了发展用户驻地网可有效地打破本地固话垄断,兼述了发展驻地网对电信管制的影响及发展驻地网所需解决的问题。     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

当代科学技术革命对马克思主义哲学的证实和发展

近半个世纪以来,现代科学沿着辩证综合的方向取得突飞猛进的发展,以信息论论证物质统一性的原理及人工智能对辩证唯物主义意识论与认识论充实与深化的内容,论证当代科学技术对马克思主义哲学的证实与发展。     

Risk,trust and knowledge networks in farmers’ learning

This study reports on New Zealand dairy farmers’ access to and use of information as mediated through conditions of risk and trust within the context of their interpersonal social networks. We located participants’ reports of their information use within their perceived environments of trust and risk, following Giddens's [1990. The consequences of modernity. Polity Press, Stanford, CA] typology of trust and risk in pre-modernity and modernity. The research participants were constant users of interpersonal and print information from numerous sources, and monitored their incoming data in the light of strategic needs, reflecting their roles as both farming practitioners and business owners. Socio-spatial knowledge networks (SSKNs) combine individuals’ explanatory cognitive models of information acquisition and use with a micro-geographical analysis of their interpersonal networks. The participants showed characteristics of pre-modern, modern and even post-modern society in respect of their use of complex interactional forms, as well as a blending of individualistic and communitarian practices and concerns in their professional and personal lives.     

应用神经元控制器的实时控制装置和实验

针对平衡倒立摆这一非线性控制问题，设计了神经元控制器和控制装置，编制了实时控制程序．控制装置运行良好，成功地平衡了倒立摆．实时控制实验显示，当倒立摆的参数变化时，神经元控制器比一般的控制器具有强得多的自适应能力．     

Slnnes：逻辑＋神经网络专家系统工具

信息与认知科学的原理表明，人工智能和网络表现了两种不同的思维方式。它具有共同的基础,这就是对信息的处理；只是处理的方法和机制各有千秋。它们相辅相成，正好构成了智能理论的完备框架。本文详细介绍的专家系统工具Slnnes就是在信息科学和认知科学理论的基础上寻求人工智能专家系统和神经网络的有机结合而形成的统一体系。