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Analog networks on function data streams

机译:函数数据流上的模拟网络

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Most of the physical processes arising in nature are modeled by differential equations, either ordinary (example: the spring/mass/damper system) or partial (example: heat diffusion). From the point of view of analog computability, the existence of an effective way to obtain solutions (either exact or approximate) of these systems is essential. A pioneering model of analog computation is the General Purpose Analog Computer (GPAC), introduced by Shannon [Journal of Mathematical Physics 20 (1941), 337–354] as a model of the Differential Analyzer and improved by Pour-El [Transactions of the American Mathematical Society 199 (1974), 1–28], Lipshitz and Rubel [Proceedings of the American Mathematical Society 99(2) (1987)], Costa and Gra?a [Journal of Complexity 19(5) (2003), 644–664] and others. The GPAC is capable of manipulating real-valued data streams. Its power is known to be characterized by the class of differentially algebraic functions, which includes the solutions of initial value problems for ordinary differential equations. We address one of the limitations of this model, which is its fundamental inability to reason about functions of more than one independent variable (the ‘time’ variable), as noted by Rubel [Advances in Applied Mathematics 14(1) (1993), 39–50]. In particular, the Shannon GPAC cannot be used to specify solutions of partial differential equations. We extend the class of data types using networks with channels which carry information on a general complete metric space X; here we take X=C(R), the class of continuous functions of one real (spatial) variable. We consider the original modules in Shannon’s construction (constants, adders, multipliers, integrators) and we add a differential module which has one input and one output. For input u, it outputs the spatial derivative v(t)=?xu(t). We then define an X-GPAC to be a network built with X-stream channels and the above-mentioned modules. This leads us to a framework in which the specifications of such analog systems are given by fixed points of certain operators on continuous data streams. Such a framework was considered by Tucker and Zucker [Theoretical Computer Science 371 (2007), 115–146]. We study the properties of these analog systems and their associated operators, and present a characterization of the X-GPAC-generable functions which generalizes Shannon’s results.
机译:自然界中产生的大多数物理过程由差分方程式建模,普通(例如:弹簧/质量/阻尼系统)或部分(例如:热扩散)。从模拟可计算性的角度来看,存在有效的方法来获得这些系统的解决方案(精确或近似)至关重要。模拟计算的先驱模型是由香农介绍的通用模拟计算机(GPAC)[数学物理学20(1941),337-354]作为差分分析仪的模型,并通过PUL-EL改善[美国数学学会199(1974),1-28],Lipshitz和Rubel [美国数学会99(2)(1987)],Costa和Gra的诉讼程序?[复杂性19(5)(2003),644 -664]和其他人。 GPAC能够操纵真实值的数据流。已知其力量以差分代数函数的表征为特征,其包括常微分方程的初始值问题的解。我们解决了该模型的一个局限性,这是它的基本上无法理解多于一个独立变量的功能('时间'变量),如Rubel所指出的[应用数学14(1)(1993)的进步, 39-50]。特别是,Shannon GPAC不能用于指定部分微分方程的解。我们使用带有频道的网络扩展数据类型的类数据类型,该通道携带关于普通完整度量空间x的信息;在这里,我们采用x = c(r),一个真实(空间)变量的连续函数的类。我们考虑Shannon施工(常量,加法器,乘数,集成商)的原始模块,我们添加了一个有一个输入和一个输出的差分模块。对于输入U,它输出空间导数v(t)=?xu(t)。然后,我们将X-GPAC定义为具有X-Stream通道的网络和上述模块。这导致我们到一个框架,其中这种模拟系统的规格由某些操作员的固定点给出了连续数据流。 Tucker和Zucker考虑了这种框架[理论计算机科学371(2007),115-146]。我们研究了这些模拟系统及其相关的运营商的属性,并展示了概述Shannon的结果的X-GPAC的函数的表征。

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