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    • 2. 发明授权
    • Method and system for process control with complex inference mechanism
    • 具有复杂推理机制的过程控制方法与系统
    • US5051932A
    • 1991-09-24
    • US328520
    • 1989-03-24
    • Haruki InoueMotohisa FunabashiMasakazu YahiroYoshiyuki Satoh
    • Haruki InoueMotohisa FunabashiMasakazu YahiroYoshiyuki Satoh
    • G05B13/00G05B13/02G06F9/44G06N5/04G06N7/04
    • G06N7/04G05B13/0295G06N5/048Y10S706/90Y10S706/906Y10S707/99935
    • A process control system for controlling a process exhibiting both linear behavior and non-linear behavior with a plurality of control quantities has a knowledge base storing therein universal facts, production rules including expert's empirical rules, meta rules describing flows of inferences, algorithm methods by mathematical expressions and membership functions referenced in fuzzy inference and a complex inference mechanism. The complex inference mechanism is composed of a complex fuzzy mechanism for inferring directly predicted values on a multi-dimensional space generated by a plurality of elements for evaluation and predicted control objectives evaluated previously with fuzzy quantities, a predicting fuzzy mechanism having the input supplied with the predicted values for determining arithmetically correlation to the membership functions determined previously with the fuzzy quantities and a satisfaction grade obtained by imparting weight to the correlation for each of the control objectives to thereby determine such a combination of the control quantities for control effectors to vary the operation states thereof from the current operation states that the satisfaction grade becomes maximum, and a main inference mechanism having the input supplied with process data to thereby compare selectively the process data with the knowledge stored in the knowledge base for thereby making decision of the process behavior through a forward fuzzy inference with the aid of the production rules based on the empirical knowledge and manage the whole process control system including the complex fuzzy inference mechanism, the predicting fuzzy inference mechanism and the knowledge base. The system can be advantageously employed for intra-tunnel ventilation control and other processes exhibiting both linear and non-linear behaviors.
    • 用于控制具有多个控制量的线性行为和非线性行为的过程的过程控制系统具有在其中存储通用事实的知识库,包括专家的经验规则的生产规则,描述推理流的元规则,通过数学的算法方法 模糊推理中引用的表达式和隶属函数以及复杂的推理机制。 复杂推理机制由复杂的模糊机制组成,用于推断由多个元素产生的多维空间上的直接预测值,用于评估以前用模糊量估计的预测控制目标,预测模糊机制具有提供的输入 用于确定与先前用模糊量确定的隶属函数的算术相关性的预测值,以及通过对每个控制目标赋予相关性而获得的满足度等级,从而确定控制效果器的控制量的组合以改变操作 来自当前操作的状态表明满意等级变为最大,并且具有输入的主推理机制提供过程数据,从而选择性地比较过程数据与知识库中存储的知识,从而进行处理行为的决定 借鉴基于实证知识的生产规则,通过前向模糊推理,对包括复杂模糊推理机制,预测模糊推理机制和知识库的整个过程控制系统进行管理。 该系统可以有利地用于隧道内通气控制和呈现线性和非线性行为的其它过程。
    • 3. 发明授权
    • Method and system for process control with complex inference mechanism
using qualitative and quantitative reasoning
    • 使用定性和定量推理的复杂推理机制的过程控制方法和系统
    • US5377308A
    • 1994-12-27
    • US013953
    • 1993-02-05
    • Haruki InoueMotohisa FunabashiMasakazu YahiroYoshiyuki Satoh
    • Haruki InoueMotohisa FunabashiMasakazu YahiroYoshiyuki Satoh
    • G05B13/02G06N5/04G06N7/04G06F15/00G06F9/44
    • G06N7/04G05B13/0295G06N5/048Y10S706/90Y10S706/906Y10S706/908Y10S706/91
    • A process control system for controlling a process exhibiting both linear behavior and non-linear behavior with a plurality of control quantities has a knowledge base storing therein universal facts, production rules including expert's empirical rules, meta rules describing flows of inferences, algorithm methods by mathematical expressions and membership functions referenced in fuzzy inference and a complex inference mechanism. The complex inference mechanism is composed of a complex fuzzy mechanism for inferring directly predicted values on a multi-dimensional space generated by a plurality of elements for evaluation and predicted control objectives evaluated previously with fuzzy quantities, a predicting fuzzy mechanism having the input supplied with the predicted values for determining arithmetically correlation to the membership functions determined previously with the fuzzy quantities and a satisfaction grade obtained by imparting weight to the correlation for each of the control objectives to thereby determine such a combination of the control quantities for control effectors to vary the operation states thereof from the current operation states that the satisfaction grade becomes maximum, and a main inference mechanism having the input supplied with process data to thereby compare selectively the process data with the knowledge stored in the knowledge base for thereby making decision of the process behavior through a forward fuzzy inference with the aid of the production rules based on the empirical knowledge and manage the whole process control system including the complex fuzzy inference mechanism, the predicting fuzzy inference mechanism and the knowledge base. The system can be advantageously employed for intra-tunnel ventilation control and other processes exhibiting both linear and non-linear behaviors.
    • 用于控制具有多个控制量的线性行为和非线性行为的过程的过程控制系统具有在其中存储通用事实的知识库,包括专家经验规则的生产规则,描述推理流的元规则,通过数学的算法方法 模糊推理中引用的表达式和隶属函数以及复杂的推理机制。 复杂推理机制由复杂的模糊机制组成,用于推断由多个元素产生的多维空间上的直接预测值,用于评估以前用模糊量估计的预测控制目标,预测模糊机制具有提供的输入 用于确定与先前用模糊量确定的隶属函数的算术相关性的预测值,以及通过对每个控制目标赋予相关性而获得的满足度等级,从而确定控制效果器的控制量的组合以改变操作 来自当前操作的状态表明满意等级变为最大,并且具有输入的主推理机制提供过程数据,从而选择性地比较过程数据与知识库中存储的知识,从而进行处理行为的决定 借鉴基于实证知识的生产规则,通过前向模糊推理,对包括复杂模糊推理机制,预测模糊推理机制和知识库的整个过程控制系统进行管理。 该系统可以有利地用于隧道内通气控制和呈现线性和非线性行为的其它过程。
    • 4. 发明授权
    • Method and system for process control with complex inference mechanism
using qualitative and quantitative reasoning
    • 使用定性和定量推理的复杂推理机制的过程控制方法和系统
    • US5251285A
    • 1993-10-05
    • US712104
    • 1991-06-07
    • Haruki InoueMotohisa FunabashiMasakazu YahiroYoshiyuki Satoh
    • Haruki InoueMotohisa FunabashiMasakazu YahiroYoshiyuki Satoh
    • G05B13/02G06N5/04G06N7/04G06F15/00
    • G06N7/04G05B13/0295G06N5/048Y10S706/90Y10S706/906Y10S706/908Y10S706/91
    • A prediction control of a process containing a non-linear behavior is effected by predicting a transition of process quantities (control quantities) of an object to be controlled a predetermined time period after the current time to provide predicted values and by determining manipulation quantities of at least a control effector in accordance with a difference between the predicted values and predetermined target values of the controlled object. A quantitative operation is carried out arithmetically identifying a process as a linear behavior, and a fuzzy inference or qualitative operation, including a fuzzy rule based on empirical knowledge, is carried out for simulating a process, wherein for input process information and current manipulation quantities to be maintained, both operations parallelly determine predicted values of control quantities. A process behavior determination is arithmetically carried out to determine whether the process linearly behaves (i.e. the normal state or the state dominant to statistic distribution) or not (i.e. the extreme transition state such as a transient response, or an indeterminate state) from the specified process information on the basis of a process behavior determination rule. As a result, a predicted value acquired by the quantitative operation is selected in response to a determination of a linear behavior, and a predicted value acquired by the fuzzy inference operation is selected in response to a determination of a non-linear behavior.
    • 包含非线性行为的处理的预测控制通过预测在当前时间之后的预定时间段内要控制的对象的处理量(控制量)的转变来提供预测值,并且通过确定在 根据控制对象的预测值和预定目标值之间的差异,至少控制效应器。 进行定量操作,将过程作为线性行为进行算术识别,进行模糊推理或定性操作,包括基于经验知识的模糊规则,用于模拟过程,其中对于输入过程信息和当前操作量 维持两个操作并行确定控制量的预测值。 算术过程执行过程行为确定,以确定过程是否从指定的过程线性表现(即,统计分布的正常状态或统计分布的统计状态)(即极端过渡状态,如瞬态响应或不确定状态) 基于过程行为确定规则处理信息。 结果,响应于线性行为的确定来选择通过定量操作获取的预测值,并且响应于非线性行为的确定来选择通过模糊推理操作获取的预测值。