Method and system for process control with complex inference mechanism using qualitative and quantitative reasoning
February 5, 2013. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesApparatus for estimating traffic condition value of elevators Water quality early warning system Methods and apparatus for system fault diagnosis and control Methods of servicing an elevator system Automated rule based process control method with feedback and apparatus therefor Inference system Antiskid brake control system based on fuzzy inference Reasoning system for reasoning with uncertainty Expert system using deep knowledge and shallow knowledge Discrete event simulation tool for analysis of qualitative models of continuous processing system InventorsAssigneeApplicationNo. 013953 filed on 02/05/1993US Classes:706/52, Reasoning under uncertainty (e.g., fuzzy logic)700/40, Plural modes706/900, FUZZY LOGIC706/906Process plantExaminersPrimary: Downs, Robert W.Attorney, Agent or FirmForeign Patent References
International ClassesG06F 015/00G06F 009/44 Foreign Application Priority Data1988-03-25 JPAbstractA 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.Other References
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