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Fixed-weight recurrent neural network controller with fixed long-term and adaptive short-term memory

机译:具有固定长期和自适应短期记忆的固定权重递归神经网络控制器

摘要

A controller for a plant having a fixed-weight recurrent neural network with at least one external input signal representative of a desired condition of the plant and actual condition of the plant, and an output connected as a control signal to the plant. The fixed recurrent neural network includes a set of nodes with fixed weight interconnections between the nodes and at least one feedback input interconnecting an output from at least one of the nodes to an input of at least one node. These nodes collectively determine the value of the output from the neural network as a function of the input signal and the feedback input. The controller also includes an adaptive neural network having a plurality of nodes with variable weight interconnections between the nodes. A cost input from the plant is connected to the adaptive neural network while an output from the adaptive neural network is coupled as a processed feedback signal to nodes of the fixed-weight recurrent neural network.
机译:用于植物的控制器,其具有固定权重的递归神经网络,该神经网络具有至少一个表示植物的期望状况和植物的实际状况的外部输入信号,以及作为控制信号连接至植物的输出。固定循环神经网络包括一组节点,这些节点在节点之间具有固定权重互连,并且至少一个反馈输入将来自至少一个节点的输出与至少一个节点的输入互连。这些节点根据输入信号和反馈输入共同确定神经网络输出的值。控制器还包括具有多个节点的自适应神经网络,节点之间具有可变权重的互连。来自工厂的成本输入连接到自适应神经网络,而来自自适应神经网络的输出作为已处理的反馈信号耦合到固定权重递归神经网络的节点。

著录项

  • 公开/公告号US7647284B2

    专利类型

  • 公开/公告日2010-01-12

    原文格式PDF

  • 申请/专利权人 DANIL V. PROKHOROV;

    申请/专利号US20070622766

  • 发明设计人 DANIL V. PROKHOROV;

    申请日2007-01-12

  • 分类号G06N3/02;

  • 国家 US

  • 入库时间 2022-08-21 18:49:59

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