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1. 发明授权

US5537339A Method of operating a plurality of utilities, a utilities network system for carrying out the method and a control device for such a network 失效
标题翻译：操作多个实用程序的方法，用于执行该方法的公用事业网络系统和用于这种网络的控制装置
公开(公告)号：US5537339A
公开(公告)日：1996-07-16
申请号：US197278
申请日：1994-02-16
申请人： Yoshio Naganuma , Akihiro Yamada , Ichiro Enbutsu , Kenji Baba
发明人： Yoshio Naganuma , Akihiro Yamada , Ichiro Enbutsu , Kenji Baba
IPC分类号： B65G61/00 , H02J3/14 , H02J13/00 , G06F19/00
CPC分类号： H02J13/0062 , H02J3/14 , Y02B70/3225 , Y02B90/2638 , Y04S20/222 , Y04S40/124
摘要： In order to operate a plurality of utilities, the utilities are interconnected via a communication path. Each utility has a control device for controlling the utility and a memory for storing information factors which are used by the corresponding control device. The information factors corresponding to all the utilities are investigated by an extraction device to determine which are common to the utilities and which are related. Where the common information factors are identified, they are passed by the communication path to the memories of all the utilities. Similarly, when related information factors are found, a calculating device calculates common information which is also passed via the communication path to the memories of all the utilities. In this way, the utilities may operate more efficiently than when they are operated on a stand-alone basis. The investigation of the information factors, and the calculating of common information from related information factors may be distributed among the utilities or carried out by a common control device. The utilities may be electric power supply systems, district heating and cooling systems, sewage disposal systems, waste recovery systems, etc.
摘要翻译：为了操作多个实用程序，实用程序通过通信路径相互连接。每个实用程序具有用于控制效用的控制装置和用于存储由相应控制装置使用的信息因子的存储器。所有实用程序对应的信息因素由提取装置进行调查，以确定哪些是公用事业公用的，哪些是相关的。在识别公共信息因素的地方，它们被通信路径传递给所有公用设施的记忆。类似地，当找到相关信息因子时，计算装置计算也通过通信路径传递给所有公用设施的存储器的公共信息。以这种方式，公用事业可以比在独立的基础上运行时更有效地运行。信息因素的调查以及相关信息因素的共同信息的计算可以在公用事业中分配，也可以由公共控制装置进行。公用事业可以是电力供应系统，区域供热和制冷系统，污水处理系统，废物回收系统等。

2. 发明授权

US5845052A Supporting method and system for process operation 失效
标题翻译：过程操作的支持方法和系统
公开(公告)号：US5845052A
公开(公告)日：1998-12-01
申请号：US582018
申请日：1996-01-02
申请人： Kenji Baba , Ichiro Enbutsu , Shoji Watanabe , Hayao Yahagi , Fumio Maruhashi , Harumi Matsuzaki , Hiroshi Matsumoto , Shunsuke Nogita , Mikio Yoda , Naoki Hara
发明人： Kenji Baba , Ichiro Enbutsu , Shoji Watanabe , Hayao Yahagi , Fumio Maruhashi , Harumi Matsuzaki , Hiroshi Matsumoto , Shunsuke Nogita , Mikio Yoda , Naoki Hara
IPC分类号： G05B13/02 , G06F9/44 , G06F15/18 , G06N3/08 , G06N5/04 , G06N7/02 , G06K9/00
CPC分类号： G05B13/027 , G05B13/0285 , Y10S706/903 , Y10S706/906
摘要： A method for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.
摘要翻译：一种用于使神经电路模型学习过程的典型过去控制结果并使用所述神经电路模型来支持所述过程的操作的方法。通过使用神经电路模型作为输入信号，在不同的时间点使用输入变量的典型值，并且作为教师信号，控制变量的相应值。将输入变量的未校正模式输入到如此学习的神经元电路模型中，由此确定控制变量的对应值。优选地，以给定时间间隔的多个图案可以同时用作要学习的图案。

3. 发明授权

US5774633A Supporting neural network method for process operation 失效
标题翻译：支持过程操作的神经网络方法
公开(公告)号：US5774633A
公开(公告)日：1998-06-30
申请号：US613718
申请日：1991-12-23
申请人： Kenji Baba , Ichiro Enbutsu , Shoji Watanabe , Hayao Yahagi , Fumio Maruhashi , Harumi Matsuzaki , Hiroshi Matsumoto , Shunsuke Nogita , Mikio Yoda , Naoki Hara
发明人： Kenji Baba , Ichiro Enbutsu , Shoji Watanabe , Hayao Yahagi , Fumio Maruhashi , Harumi Matsuzaki , Hiroshi Matsumoto , Shunsuke Nogita , Mikio Yoda , Naoki Hara
IPC分类号： G05B13/02 , G06F9/44 , G06F15/18 , G06N3/08 , G06N5/04 , G06N7/02
CPC分类号： G05B13/027 , G05B13/0285 , Y10S706/903 , Y10S706/906
摘要： A method and a system for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.
摘要翻译： PCT No.PCT / JP90 / 00322 Sec。 371 1991年12月23日第 102（e）1991年12月23日PCT PCT 1990年3月13日PCT公布。出版物WO90 / 10898 1990年9月20日1990年一种使神经电路模型学习过程的典型过去控制结果并使用神经电路模型支持过程操作的方法和系统。通过使用神经电路模型作为输入信号，在不同的时间点使用输入变量的典型值，并且作为教师信号，控制变量的相应值。将输入变量的未校正模式输入到如此学习的神经元电路模型中，由此确定控制变量的对应值。优选地，以给定时间间隔的多个图案可以同时用作要学习的图案。

4. 发明授权

US5324431A Process for treating suspended substances in liquor 失效
标题翻译：处理酒中悬浮物质的方法
公开(公告)号：US5324431A
公开(公告)日：1994-06-28
申请号：US571924
申请日：1990-08-24
申请人： Shoji Watanabe , Kenji Baba , Ichiro Enbutsu , Hayao Yahagi , Harumi Matsuzaki , Mikio Yoda , Naoki Hara , Yoshikatsu Asada
发明人： Shoji Watanabe , Kenji Baba , Ichiro Enbutsu , Hayao Yahagi , Harumi Matsuzaki , Mikio Yoda , Naoki Hara , Yoshikatsu Asada
IPC分类号： G01N21/17 , B01D21/01 , B01D24/48 , B01D29/46 , B01D29/60 , B01D29/62 , C02F1/58 , C02F3/00 , C02F3/12 , C02F3/34 , C12Q1/02 , G01N15/04 , G01N33/18 , G05D21/02 , C02F3/02
CPC分类号： G05D21/02 , C02F3/006 , C02F3/34 , G01N33/1866
摘要： Suspended substances, such as active sludge, in a liquor is treated in an apparatus for treating the liquor by predicting a suspension state on the basis of an amount of existing suspended substances in the liquor.
摘要翻译：液体中的悬浮物质如活性污泥在用于处理液体的装置中通过基于液体中现有悬浮物质的量预测悬浮状态进行处理。

5. 发明授权

US5943662A Supporting method and system for process operation 失效
标题翻译：过程操作的支持方法和系统
公开(公告)号：US5943662A
公开(公告)日：1999-08-24
申请号：US220546
申请日：1994-03-31
申请人： Kenji Baba , Ichiro Enbutsu , Shoji Watanabe , Hayao Yahagi , Fumio Maruhashi , Harumi Matsuzaki , Hiroshi Matsumoto , Shunsuke Nogita , Mikio Yoda , Naoki Hara
发明人： Kenji Baba , Ichiro Enbutsu , Shoji Watanabe , Hayao Yahagi , Fumio Maruhashi , Harumi Matsuzaki , Hiroshi Matsumoto , Shunsuke Nogita , Mikio Yoda , Naoki Hara
IPC分类号： G05B13/02 , G06F9/44 , G06F15/18 , G06N3/08 , G06N5/04 , G06N7/02
CPC分类号： G05B13/027 , G05B13/0285 , Y10S706/903 , Y10S706/906
摘要： A method and a system for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.
摘要翻译：一种用于使神经电路模型学习过程的典型过去控制结果并使用所述神经电路模型来支持所述过程的操作的方法和系统。通过使用神经电路模型作为输入信号，在不同的时间点使用输入变量的典型值，并且作为教师信号，控制变量的相应值。将输入变量的未校正模式输入到如此学习的神经元电路模型中，由此确定控制变量的对应值。优选地，以给定时间间隔的多个图案可以同时用作要学习的图案。

6. 发明授权

US5581459A Plant operation support system 失效
标题翻译：工厂运行支持系统
公开(公告)号：US5581459A
公开(公告)日：1996-12-03
申请号：US765839
申请日：1991-09-26
申请人： Ichiro Enbutsu , Kenji Baba , Shoji Watanabe , Hayao Yahagi , Naoki Hara , Mikio Yoda
发明人： Ichiro Enbutsu , Kenji Baba , Shoji Watanabe , Hayao Yahagi , Naoki Hara , Mikio Yoda
IPC分类号： G05B13/02 , G06N3/04 , G06F15/18
CPC分类号： G06N3/0427 , G05B13/027 , Y10S706/903 , Y10S706/906
摘要： When amounts of operation control variables and/or operation state evaluation variables of a plant are calculated through a multilayered neural network based on data indicating a plant state, and the calculated results are indicated to an operator, a plant operation support system has functions to analyze an internal causality between neurons in the neural network, and display quantitative guidance by association in the neural network and also the analyzed result of the internal causality between the neurons as an association reason.
摘要翻译：当基于指示工厂状态的数据通过多层神经网络计算工厂的操作控制变量和/或操作状态评估变量的数量时，将运算结果指示给操作者，工厂操作支持系统具有分析功能神经网络中神经元之间的内在因果关系，并通过神经网络中的关联显示定量指导，以及神经元内部因果关系的分析结果作为关联原因。

7. 发明授权

US5465320A Method of automated learning, an apparatus therefor, and a system incorporating such an apparatus 失效
标题翻译：自动学习方法，其装置以及包含这种装置的系统
公开(公告)号：US5465320A
公开(公告)日：1995-11-07
申请号：US907539
申请日：1992-07-02
申请人： Ichiro Enbutsu , Kenzi Baba , Naoki Hara , Mikio Yoda , Shoji Watanabe , Hayao Yahagi
发明人： Ichiro Enbutsu , Kenzi Baba , Naoki Hara , Mikio Yoda , Shoji Watanabe , Hayao Yahagi
IPC分类号： G05B13/02 , G06F9/44 , G06F15/18 , G06N3/00 , G06N3/04 , G06N5/04 , G06N7/02 , G06F9/00
CPC分类号： G06N5/025 , G05B13/0285 , G06N3/0436 , Y10S706/90
摘要： In order to speed up and simplify automated learning of rules by a neural network making use of fuzzy logic, data from a system is analyzed by a teaching data creation means which groups the data into clusters and then selects a representative data item from each group for subsequent analysis. The selected data items are passed to a rule extraction means which investigates relationships between the data items, to derive rules, but eliminates rules which have only an insignificant effect on the system. The results are candidate rules which are stored in a first rule base. The candidate rules are then compared with rules in a second rule base to check for duplication and/or contradiction. Only those rules which are not duplicated and not contradictory are stored in the second rule base. Hence, when fuzzy inference is used to control the system on the basis of rules in the second rule base, only valid rules which provide a significant effect on the system are used.
摘要翻译：为了加快和简化由使用模糊逻辑的神经网络自动学习规则，通过教学数据创建装置分析来自系统的数据，其将数据分组成簇，然后从每组中选择代表性的数据项，后续分析。所选择的数据项被传递到规则提取装置，该装置调查数据项之间的关系，导出规则，但消除对系统仅具有微不足道的影响的规则。结果是存储在第一规则库中的候选规则。然后将候选规则与第二规则库中的规则进行比较，以检查重复和/或矛盾。只有那些不重复而不矛盾的规则才存储在第二个规则库中。因此，当使用模糊推理来基于第二规则库中的规则来控制系统时，仅使用对系统产生重大影响的有效规则。

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