Fuzzy rule generation from data for process operational decision support

Wang, X.Z.; Chen, B.H.; Yang, S.H.; McGreavy, C.; Lu, Mingliang

doi:10.1016/s0098-1354(97)87578-3

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…3). The structure of the toolbox is based on the architecture proposed by Wang et al [9]. The training process of the FUN toolbox is as follows: User selects the columns of the excel data that will be used as input(s)-output(s) variables to the system, and the corresponding fuzzy sets domain space.…”

Section: Methodsmentioning

confidence: 99%

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A Neuro-Fuzzy Controller for the estimation of Tidal Volume and Respiration Frequency ventilator settings for COPD patients ventilated in control mode

Tzavaras

Weller

Spyropoulos

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Patients with chronic obstructive pulmonary disease (COPD) are characterized by increased work of breathing (WOB) and ventilatory muscle dysfunction. Mechanical ventilation is applied to unload the WOB; rest respiratory muscles decrease arterial partial pressure of carbon dioxide (PaCO2) and treat hypoxemia. Since patients' needs are not static, ventilator settings have to be adjusted regularly. The aim of the present study was the development and evaluation of a neuro-fuzzy controller, that utilizes non-invasively acquired parameters for the determination of the appropriate tidal volume (VT) and respiration frequency (RR) ventilator settings for COPD patients. Forty three (43) hours of non-invasively monitored physiology parameters and ventilator settings, from four (4) different COPD patients ventilated in control mode, were collected in two (2) General Hospitals in Greece. Recorded data were randomly allocated into two sets, namely training set (60%) and evaluation set (40%). A neuro-fuzzy controller was developed and trained, by employing the training set. The controller utilizes non-invasively measured parameters, namely oxygen saturation (SpO2), lung compliance (C) and resistance (R), Peak Inspiratory pressure (PIP) and Plateau pressure (Pplateau), for predicting appropriate VT and RR settings. The developed neuro-fuzzy controller was tested against evaluation set. The Mean Square Error of the tidal volume and the respiration rate was 0.222 ml/Kgr and 1.21 breaths per minute (bpm) respectively.

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Section: Methodsmentioning

confidence: 99%

“…X.Z. Wang et al proposed a neural fuzzy network for RB generation [9]. The Input and Output variable domains are assigned to Linguistic Variables.…”

Section: B Synergism Of Frbss and Nnmentioning

confidence: 99%

A Neuro-Fuzzy Controller for the estimation of Tidal Volume and Respiration Frequency ventilator settings for COPD patients ventilated in control mode

Tzavaras

Weller

Spyropoulos

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Knowledge-based expert systems make use of expertise and experience of human experts. Previous research on automatically extracting fuzzy rules from operational data has been reported (Wang et al, 1997a). Neural network-based machine learning uses data to train the networks, but the way of making use of data depends on the type of learning (i.e., supervised or unsupervised).…”

Section: Operational Status Classification Of a Fluid Catalytic Crack...mentioning

confidence: 99%

Automatic Classification for Mining Process Operational Data

Wang

McGreavy

1998

Ind. Eng. Chem. Res.

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In process plant operation and control, modern distributed control and automatic data logging systems create large volumes of data that contain valuable information about normal and abnormal operations, significant disturbances, and changes in operational and control strategies. These data have tended to be underexploited for a variety of reasons, including the large volume and lack of effective automatic computer-based support tools. This paper considers a data mining system that is able to automatically cluster the data into classes corresponding to various operational modes and thereby provide some structure for analysis of behavioral responses. The method is illustrated by reference to a case study of a refinery fluid catalytic cracking process.

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“…These techniques belong to different areas of the artificial intelligence, such as case-based reasoning 10,11 and soft computing. [12][13][14][15] Other authors suggest the integration of different techniques to develop hybrid systems as the most optimal solution. [16][17][18] This paper presents a particular case in which the application of a modular KBS has contributed enhancement of the control of a full-scale WWTP.…”

Section: Introductionmentioning

confidence: 99%

“…The stepping stone that involves processing the information to acquire and fix the knowledge has led to the development of different supporting tools that avoid the use of heuristic knowledge. These techniques belong to different areas of the artificial intelligence, such as case-based reasoning , and soft computing. − Other authors suggest the integration of different techniques to develop hybrid systems as the most optimal solution. − …”

Section: Introductionmentioning

confidence: 99%

Automatic Knowledge Acquisition from Complex Processes for the Development of Knowledge-Based Systems

R-Roda¹,

Comas²,

Poch³

et al. 2001

Ind. Eng. Chem. Res.

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A knowledge-based system for the supervision of a wastewater treatment plant was successfully applied to a full-scale facility. The key factor of this supporting tool development was the two-phase methodology used to acquire and fix the knowledge into the knowledge base. Both phases of the methodology are presented in the paper; the first consists of literature reviews and site interviews with domain experts, while the second is based on machine learning tools and is subdivided into four steps: data handling, classification, interpretation, and codification. The aim of this two-phase methodology is meant to ease the knowledge acquisition process. The main objective is to find the relevant issues and then reduce the space of search according to the target facility. Also, this methodology allowed the user to explore the data space to discover, if any exist, new pieces of knowledge. This methodology can be generalized to acquire specific knowledge from any (bio)chemical process, improving the development process and the efficiency of the supervisory knowledge-based system.

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Fuzzy rule generation from data for process operational decision support

Cited by 15 publications

References 6 publications

A Neuro-Fuzzy Controller for the estimation of Tidal Volume and Respiration Frequency ventilator settings for COPD patients ventilated in control mode

A Neuro-Fuzzy Controller for the estimation of Tidal Volume and Respiration Frequency ventilator settings for COPD patients ventilated in control mode

Automatic Classification for Mining Process Operational Data

Automatic Knowledge Acquisition from Complex Processes for the Development of Knowledge-Based Systems

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