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ADAPTIVE SELF-ORGANIZING LOGIC NETWORKS (PARALLEL, NEURAL MODELS, CONNECTIONIST, REAL-TIME, DISTRIBUTED).

机译:自适应的自组织逻辑网络(并行,神经模型,连接器,实时,分布式)。

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摘要

Along with the development of contemporary computer science the limitations of sequential "von Neumann" machines have become more apparent. It is now becoming clear that to handle projected needs in speed and throughput, massively parallel architectures will be needed.;Problem specificiation is incremental and takes the form of if-then rules (instances) expressed as Boolean conjunctions. Possible applications include symbolic decision systems, propositional production systems, digital pattern recognition and real-time process control.;The approach is based on an adaptive network composed of many simple computing elements (nodes) which operate in a combinational and asynchronous fashion. Control and processing in the network is distributed amongst the network nodes. Adaptation and data processing form two separate phases of operation. During processing, the network acts as a parallel network of Boolean gates. Inputs and outputs of the network are also Boolean. During adaptation the network structure and the node functions can change to update the overall network function as specified. As new rules are added to the rule base, the network independently reconfigures to a logic circuit that remains both minimal and consistent with the rule base. Thus, there is no explicit programming. Desired network response is simply presented to the system, following which the network adjusts itself accordingly. Although the functionality of the network can be observed from the outside, the internal network structure is unknown.;The control of the adaptive process is almost completely distributed and efficiently exploits parallelism. Most communication takes place between neighboring nodes with only minimal need for centralized processing. The network modification is performed with considerable concurrency and the adaptation time grows only linearly with the depth of the network.;In this dissertation we propose a special purpose architectural model that satisfies a general class of propositional logic problems in a totally distributed and concurrent fashion. The architectural model is identified as ASOCS (Adaptive Self-Organizing Concurrent System).
机译:随着当代计算机科学的发展,顺序“冯·诺依曼”机器的局限性变得更加明显。现在越来越清楚的是,要满足速度和吞吐量方面的计划需求,将需要大规模并行体系结构。问题指定是递增的,并采用if-then规则(实例)的形式表示为布尔连接。可能的应用包括符号决策系统,命题生产系统,数字模式识别和实时过程控制。该方法基于一个自适应网络,该网络由许多以组合和异步方式运行的简单计算元素(节点)组成。网络中的控制和处理分布在网络节点之间。适应和数据处理形成两个独立的操作阶段。在处理期间,该网络充当布尔门的并行网络。网络的输入和输出也是布尔值。在自适应期间,网络结构和节点功能可以更改,以更新指定的整体网络功能。当将新规则添加到规则库时,网络将独立地重新配置为逻辑电路,该逻辑电路保持最小且与规则库一致。因此,没有显式的编程。所需的网络响应会简单地呈现给系统,然后网络会相应地进行自我调整。尽管可以从外部观察网络的功能,但是内部网络结构是未知的。自适应过程的控制几乎完全分布并且有效地利用了并行性。大多数通信发生在相邻节点之间,只需要很少的集中处理。网络修改是在相当大的并发下进行的,适应时间仅随着网络的深度线性增长。本文提出了一种特殊目的的体系结构模型,该模型以一种完全分布式和并行的方式满足一般的命题逻辑问题。该架构模型被标识为ASOCS(自适应自组织并发系统)。

著录项

  • 作者

    MARTINEZ, TONY RAMON.;

  • 作者单位

    University of California, Los Angeles.;

  • 授予单位 University of California, Los Angeles.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 1986
  • 页码 289 p.
  • 总页数 289
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:51:01

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