[특허]Hybrid linear-neural network process control

Hybrid linear-neural network process control 원문보기

IPC분류정보
국가/구분	United States(US) Patent 등록
국제특허분류(IPC7판)	G06F-007/60 G06F-017/10 G06F-101/00 G06F-017/50
출원번호	US-0165854 (1998-10-02)
발명자 / 주소	Klimasauskas Casimir C. Guiver John P.
출원인 / 주소	Aspen Technology, Inc.
대리인 / 주소	Bracewell & Patterson, L.L.P.
인용정보	피인용 횟수 : 43 인용 특허 : 0

초록 ▼

A hybrid analyzer having a data derived primary analyzer and an error correction analyzer connected in parallel is disclosed. The primary analyzer, preferably a data derived linear model such as a partial least squares model, is trained using training data to generate major predictions of defined output variables. The error correction analyzer, preferably a neural network model is trained to capture the residuals between the primary analyzer outputs and the target process variables. The residuals generated by the error correction analyzer is summed with the output of the primary analyzer to compensate for the error residuals of the primary analyzer to arrive at a more accurate overall model of the target process. Additionally, an adaptive filter can be applied to the output of the primary analyzer to further capture the process dynamics. The data derived hybrid analyzer provides a readily adaptable framework to build the process model without requiring up-front knowledge. Additionally, the primary analyzer, which incorporates the PLS model, is well accepted by process control engineers. Further, the hybrid analyzer also addresses the reliability of the process model output over the operating range since the primary analyzer can extrapolate data in a predictable way beyond the data used to train the model. Together, the primary and the error correction analyzers provide a more accurate hybrid process analyzer which mitigates the disadvantages, and enhances the advantages, of each modeling methodology when used alone.

대표청구항 ▼

[ What is claimed:] [1.]1. A method for modeling a process having one or more disturbance variables as process input conditions, one or more corresponding manipulated variables as process control conditions, and one or more corresponding controlled variables as process output conditions, said method comprising:picking one or more selected variables from said disturbance variables and said manipulated variables;providing said selected variables to a data derived primary analyzer and an error correction analyzer;generating a primary output from said selected variables using said data derived primary analyzer;generating a predicted error output from said selected variables using said error correction analyzer;summing the output of said primary and error correction analyzers;presenting said summed output to a distributed control system;selecting and time-shifting pre-determining variables from said distributed control system using a run-time delay and variable selector;presenting the output of said run-time delay and variable selector to said data derived primary analyzer and said error correction analyzer;picking one or more training variables from disturbance variables and manipulated variables stored in said data repository, said training variables having a corresponding training controlled variable;determining, said delay and variable settings from said training variables;providing said training variables to a training primary analyzer and a training error correction analyzer;generating a training primary output from said training variables using said training primary analyzer;subtracting said training primary output from said training controlled variable to generate a feedback variable;generating a predicted training error output from said training variables and said feedback variable using said training error correction analyzer, wherein said generating a predicted training error output further includes training a neural network partial least squares error correction analyzer, wherein said neural network partial least squares error correction analyzer has a non-linear function f(t.sub.h) and an error function, wherein said training input vector is defined as rein said training primary output is defined as rein Y further equals TBQ'+F, further comprising the step of minimizing said error function .parallel.u.sub.h -f(t.sub.h).parallel..sup.2 in said neural network partial least squares error correction analyzer;summing said training primary output and said predicted training error output;updating said delay and variable settings and said model parameters;computing a difference between said summed output of said summed training primary output and said predicted training error output and said training controlled variable;repeating said step of determining delay and variable through said step of computing a difference until said the performance of said analyzer on a test data set reaches an optimum point;storing said delay and variable settings in said run-time delay and variable selector;storing said model parameters in said data derived primary analyzer and said error correction analyzer.

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IPC	Description
A	생활필수품
A62	인명구조; 소방(사다리 E06C)
A62B	인명구조용의 기구, 장치 또는 방법(특히 의료용에 사용되는 밸브 A61M 39/00; 특히 물에서 쓰이는 인명구조 장치 또는 방법 B63C 9/00; 잠수장비 B63C 11/00; 특히 항공기에 쓰는 것, 예. 낙하산, 투출좌석 B64D; 특히 광산에서 쓰이는 구조장치 E21F 11/00)
A62B-1/08	.. 윈치 또는 풀리에 제동기구가 있는 것

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Hybrid linear-neural network process control 원문보기

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