Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251143
E. Mugambi, A. Hunter
Comprehensibility is an important factor in medical predictive modelling as it dictates the credibility and even acceptability of a model. Generally, the performance of a model has always been the primary focus in most data mining jobs. Where there are serious risks posed by the decisions made by a model, it is not feasible to view comprehensibility aspects of a model as secondary to performance. While model comprehensibility is a topic that has aroused a lot of interest with two conference workshops (AI-UCAI'95 & AAAI 2005) placing it as its keynote issue and many papers written about it, there are no empirical methods of measuring it or even one consistent way to define it. It is generally accepted that smaller models are more comprehensible than larger ones. This forms the basis of most researches conducted in this area. In this paper, we investigate the efficacy of using multiobjective optimization in the Pareto sense to meet comprehensibility demands of models. Some of the objective functions used in this paper are novel while others have been used in other researches before. The results obtained show that incorporating aspects of comprehensibility in the induction process models does not necessarily retard the performance of models and could actually improve the performance versus complexity trade-off of evolutionary polynomial-fuzzy structures
{"title":"Inducing Comprehensibility In Evolutionary Polynomial-Fuzzy Classification Models","authors":"E. Mugambi, A. Hunter","doi":"10.1109/ISEFS.2006.251143","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251143","url":null,"abstract":"Comprehensibility is an important factor in medical predictive modelling as it dictates the credibility and even acceptability of a model. Generally, the performance of a model has always been the primary focus in most data mining jobs. Where there are serious risks posed by the decisions made by a model, it is not feasible to view comprehensibility aspects of a model as secondary to performance. While model comprehensibility is a topic that has aroused a lot of interest with two conference workshops (AI-UCAI'95 & AAAI 2005) placing it as its keynote issue and many papers written about it, there are no empirical methods of measuring it or even one consistent way to define it. It is generally accepted that smaller models are more comprehensible than larger ones. This forms the basis of most researches conducted in this area. In this paper, we investigate the efficacy of using multiobjective optimization in the Pareto sense to meet comprehensibility demands of models. Some of the objective functions used in this paper are novel while others have been used in other researches before. The results obtained show that incorporating aspects of comprehensibility in the induction process models does not necessarily retard the performance of models and could actually improve the performance versus complexity trade-off of evolutionary polynomial-fuzzy structures","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128140929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251158
A. Otero, J. Otero, L. Sanchez, J. Villar
Measuring the length of a path that a taxi must fare for is not an obvious task. When driving lower than certain threshold the fare is time dependent, but at higher speeds the length of the path is measured, and the fare depends on such measure. When passing an indoor MOT test, the taximeter is calibrated simulating a cab run, while the taxi is placed on a device equipped with four rotating steel cylinders in touch with the drive wheels. This indoor measure might be inaccurate, as information given by the cylinders is affected by tires inflating pressure, and only straight trajectories are tested. Moreover, modern vehicles with driving aids such as ABS, ESP or TCS might have their electronics damaged in the test, since two wheels are spinning while the others are not. To overcome these problems, we have designed a small, portable GPS sensor that periodically logs the coordinates of the vehicle and computes the length of a discretionary circuit. We show that all the legal issues with the tolerance of such a procedure (GPS data are inherently imprecise) can be overcome if genetic and fuzzy techniques are used to preprocess and analyze the raw data
{"title":"Longest path estimation from inherently fuzzy data acquired with GPS using genetic algorithms","authors":"A. Otero, J. Otero, L. Sanchez, J. Villar","doi":"10.1109/ISEFS.2006.251158","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251158","url":null,"abstract":"Measuring the length of a path that a taxi must fare for is not an obvious task. When driving lower than certain threshold the fare is time dependent, but at higher speeds the length of the path is measured, and the fare depends on such measure. When passing an indoor MOT test, the taximeter is calibrated simulating a cab run, while the taxi is placed on a device equipped with four rotating steel cylinders in touch with the drive wheels. This indoor measure might be inaccurate, as information given by the cylinders is affected by tires inflating pressure, and only straight trajectories are tested. Moreover, modern vehicles with driving aids such as ABS, ESP or TCS might have their electronics damaged in the test, since two wheels are spinning while the others are not. To overcome these problems, we have designed a small, portable GPS sensor that periodically logs the coordinates of the vehicle and computes the length of a discretionary circuit. We show that all the legal issues with the tolerance of such a procedure (GPS data are inherently imprecise) can be overcome if genetic and fuzzy techniques are used to preprocess and analyze the raw data","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115912135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251153
G. Vachkov
This paper proposes a technology for numerical comparison of different operating states in construction machines and other complex systems, working in frequently changing modes and under variable loads. The results from the comparison can be used for detailed operations recognition and fault diagnosis. The raw data from each operation are represented in a compressed form by a neural model. A special "growing model learning" algorithm is proposed in the paper and compared with the standard "fixed model learning" algorithm. Results from a test example show the superiority of the growing learning algorithm in terms of computation time and its ability to guarantee the predetermined model accuracy. Two methods for numerical comparison of pairs of operations, which utilize the trained neural models, are also proposed in the paper. They use the center-of-gravity and the relative size of each operation. Finally, an application of the methods to the comparison and recognition of eight operating states of hydraulic excavator is given in the paper
{"title":"Recognition of Different Operating States in Complex Systems by Use of Growing Neural Models","authors":"G. Vachkov","doi":"10.1109/ISEFS.2006.251153","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251153","url":null,"abstract":"This paper proposes a technology for numerical comparison of different operating states in construction machines and other complex systems, working in frequently changing modes and under variable loads. The results from the comparison can be used for detailed operations recognition and fault diagnosis. The raw data from each operation are represented in a compressed form by a neural model. A special \"growing model learning\" algorithm is proposed in the paper and compared with the standard \"fixed model learning\" algorithm. Results from a test example show the superiority of the growing learning algorithm in terms of computation time and its ability to guarantee the predetermined model accuracy. Two methods for numerical comparison of pairs of operations, which utilize the trained neural models, are also proposed in the paper. They use the center-of-gravity and the relative size of each operation. Finally, an application of the methods to the comparison and recognition of eight operating states of hydraulic excavator is given in the paper","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114470615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251163
E. Jones, P. Angelov, C. Xydeas
In order to deliver real time, high quality voice services in packet based voice system (e.g. voice over Internet protocol, VoIP) system designers must tackle inherent quality problems related to possible packet loss. To combat the inevitable speech quality deterioration resulting from the loss of transmitted packets of speech information, techniques that provide estimates of the lost information that is needed by the speech recovery process are of considerable interest. Furthermore, in VoIP systems employing linear predictive coding (LPC) based speech coders, a significant percentage of the coded speech information represent the values of LPC coefficients and thus a new approach for estimating missing LPC filter coefficients is presented in this paper. This approach employs a new formulation of LSP recovery system architecture where evolving fuzzy rule-based models and particularly so-called evolving Takagi-Sugeno models are deployed to generate the required estimates of missing LSPs. The proposed missing parameters estimation technique is generic and initial experimental results demonstrate its considerable potential in improving the quality of LPC based decoded speech in VoIP applications
{"title":"Recovery of LSP Coefficient in VoIP Systems using Evolving Takagi-Sugeno Fuzzy Models","authors":"E. Jones, P. Angelov, C. Xydeas","doi":"10.1109/ISEFS.2006.251163","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251163","url":null,"abstract":"In order to deliver real time, high quality voice services in packet based voice system (e.g. voice over Internet protocol, VoIP) system designers must tackle inherent quality problems related to possible packet loss. To combat the inevitable speech quality deterioration resulting from the loss of transmitted packets of speech information, techniques that provide estimates of the lost information that is needed by the speech recovery process are of considerable interest. Furthermore, in VoIP systems employing linear predictive coding (LPC) based speech coders, a significant percentage of the coded speech information represent the values of LPC coefficients and thus a new approach for estimating missing LPC filter coefficients is presented in this paper. This approach employs a new formulation of LSP recovery system architecture where evolving fuzzy rule-based models and particularly so-called evolving Takagi-Sugeno models are deployed to generate the required estimates of missing LSPs. The proposed missing parameters estimation technique is generic and initial experimental results demonstrate its considerable potential in improving the quality of LPC based decoded speech in VoIP applications","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128344362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251132
J.-C. de Barros, A. Dexter
This paper describes an evolving fuzzy model (efM) approach to modelling non-linear dynamic systems in which an incremental learning method is used to build up the rule-base. The rule-base evolves when "new" information becomes available by creating a new rule, merging an existing rule or deleting an old rule, depended upon the proximity and potential of the rules, and the maximum number of rules to be used in the rule-base. The efM, which is based on a T-S fuzzy model with constant consequents, is a very good candidate for modelling complex non-linear systems, when the period of time required to collect a complete set of training data is too long for the model to be identified off-line and the learning scheme must be computationally undemanding, e.g. use in model-based self-learning controllers. The results presented in the paper demonstrate the ability of the efM to evolve the rule-base efficiently so as to account for the behaviour of the system in new regions of the operating space. The proposed approach generates an accurate model with relatively few rules in a computationally undemanding manner, even if the data are incomplete
{"title":"An Evolving Fuzzy Model for Embedded Applications","authors":"J.-C. de Barros, A. Dexter","doi":"10.1109/ISEFS.2006.251132","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251132","url":null,"abstract":"This paper describes an evolving fuzzy model (efM) approach to modelling non-linear dynamic systems in which an incremental learning method is used to build up the rule-base. The rule-base evolves when \"new\" information becomes available by creating a new rule, merging an existing rule or deleting an old rule, depended upon the proximity and potential of the rules, and the maximum number of rules to be used in the rule-base. The efM, which is based on a T-S fuzzy model with constant consequents, is a very good candidate for modelling complex non-linear systems, when the period of time required to collect a complete set of training data is too long for the model to be identified off-line and the learning scheme must be computationally undemanding, e.g. use in model-based self-learning controllers. The results presented in the paper demonstrate the ability of the efM to evolve the rule-base efficiently so as to account for the behaviour of the system in new regions of the operating space. The proposed approach generates an accurate model with relatively few rules in a computationally undemanding manner, even if the data are incomplete","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132545468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251174
T. Martin, B. Azvine
The need for information fusion exists in the semi-structured and unstructured domains - for example, to integrate responses from multiple sources into a unified response. This can be regarded as a two stage process - first to determine whether any two sources are considering the same real-world entities, and second, to ascertain how the attributes correspond (e.g. author/composer should correspond almost exactly to creator, business-location should correspond to address, etc). Within the unstructured and semi-structured attribute values there is frequently hidden structure -e.g. a free text attribute labeled as name might consist of title, first name and family name. Revealing this structure can greatly assist the matching process. In this paper, we outline a method for approximate matching of entities from different data sources and show how an evolutionary approach can create accurate approximate grammars to aid the information integration
{"title":"Evolution of Fuzzy Grammars to aid Instance Matching","authors":"T. Martin, B. Azvine","doi":"10.1109/ISEFS.2006.251174","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251174","url":null,"abstract":"The need for information fusion exists in the semi-structured and unstructured domains - for example, to integrate responses from multiple sources into a unified response. This can be regarded as a two stage process - first to determine whether any two sources are considering the same real-world entities, and second, to ascertain how the attributes correspond (e.g. author/composer should correspond almost exactly to creator, business-location should correspond to address, etc). Within the unstructured and semi-structured attribute values there is frequently hidden structure -e.g. a free text attribute labeled as name might consist of title, first name and family name. Revealing this structure can greatly assist the matching process. In this paper, we outline a method for approximate matching of entities from different data sources and show how an evolutionary approach can create accurate approximate grammars to aid the information integration","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117015155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251159
P. Bonissone
We analyze the issue of decision-making using soft computing (SC) models. We define a natural framework in the cross product of the decision's time horizon and the type of domain knowledge used by the SC models. Within this framework, we analyze the progression from simple lexicon to annotated lexicon, morphology, syntax, semantics, and pragmatics. We compare this progression with the injection of domain knowledge in SC to perform tasks in the context of prognostics & health management (PHM), such as anomaly detection and identification (unsupervised clustering), failure mode analysis (supervised learning), prognostics of remaining useful life (prediction), on-board fault accommodation (realtime control), and off board logistics actions (decision support). Finally, we analyze evolutionary fuzzy systems (EFS) and determine their position and role in this framework
{"title":"Domain Knowledge and Decision Time: A Framework for Soft Computing Applications","authors":"P. Bonissone","doi":"10.1109/ISEFS.2006.251159","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251159","url":null,"abstract":"We analyze the issue of decision-making using soft computing (SC) models. We define a natural framework in the cross product of the decision's time horizon and the type of domain knowledge used by the SC models. Within this framework, we analyze the progression from simple lexicon to annotated lexicon, morphology, syntax, semantics, and pragmatics. We compare this progression with the injection of domain knowledge in SC to perform tasks in the context of prognostics & health management (PHM), such as anomaly detection and identification (unsupervised clustering), failure mode analysis (supervised learning), prognostics of remaining useful life (prediction), on-board fault accommodation (realtime control), and off board logistics actions (decision support). Finally, we analyze evolutionary fuzzy systems (EFS) and determine their position and role in this framework","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123191210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251162
V. Lokhin, S. Manko, M. Romanov, I. Gartseev, M. V. Kadochnikov
The possibility of efficient functioning in a priori undefined and changeable conditions, being one of the major features of intelligent systems, is mostly predefined by their abilities in self-education and self-organization. Therefore the problems of generalizing acquired experience, automatically forming and augmenting knowledge are both interesting academically and significant for applications. The elaboration of the existing approaches and the development of new ways of solving these problems provides a substantial basis for the creation of intelligent self-educating systems of various types and purposes, possessing a wide set of abilities in adapting one's behavior to the environment's actions, forecasting the changes of situation, exposing the existing patterns, etc. One of the most interesting and promising approaches to the problem of automatic knowledge base synthesis for intelligent control systems is connected with the use of so-called genetic algorithms
{"title":"Automatic education and self organization of intelligent robotic systems based on genetic algorithms","authors":"V. Lokhin, S. Manko, M. Romanov, I. Gartseev, M. V. Kadochnikov","doi":"10.1109/ISEFS.2006.251162","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251162","url":null,"abstract":"The possibility of efficient functioning in a priori undefined and changeable conditions, being one of the major features of intelligent systems, is mostly predefined by their abilities in self-education and self-organization. Therefore the problems of generalizing acquired experience, automatically forming and augmenting knowledge are both interesting academically and significant for applications. The elaboration of the existing approaches and the development of new ways of solving these problems provides a substantial basis for the creation of intelligent self-educating systems of various types and purposes, possessing a wide set of abilities in adapting one's behavior to the environment's actions, forecasting the changes of situation, exposing the existing patterns, etc. One of the most interesting and promising approaches to the problem of automatic knowledge base synthesis for intelligent control systems is connected with the use of so-called genetic algorithms","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132335118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251149
H. Ishibuchi, Y. Nojima, I. Kuwajima
We examine the effect of genetic rule selection as a postprocessing procedure in fuzzy data mining. Usually a large number of fuzzy rules are extracted in a heuristic manner from numerical data using a rule evaluation criterion in fuzzy data mining. It is, however, very difficult for human users to understand thousands of fuzzy rules. Thus it is necessary to decrease the number of extracted fuzzy rules when our task is to present understandable knowledge to human users. In this paper, we use genetic rule selection to decrease the number of extracted fuzzy rules. Through computational experiments, we examine the effect of genetic rule selection. First we extract fuzzy rules that satisfy minimum support and confidence levels. Thousands of fuzzy rules are extracted from numerical data in a heuristic manner. Then we apply genetic rule selection to extracted fuzzy rules. Experimental results show that genetic rule selection significantly decreases the number of extracted fuzzy rules without degrading their classification accuracy
{"title":"Genetic Rule Selection as a Postprocessing Procedure in Fuzzy Data Mining","authors":"H. Ishibuchi, Y. Nojima, I. Kuwajima","doi":"10.1109/ISEFS.2006.251149","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251149","url":null,"abstract":"We examine the effect of genetic rule selection as a postprocessing procedure in fuzzy data mining. Usually a large number of fuzzy rules are extracted in a heuristic manner from numerical data using a rule evaluation criterion in fuzzy data mining. It is, however, very difficult for human users to understand thousands of fuzzy rules. Thus it is necessary to decrease the number of extracted fuzzy rules when our task is to present understandable knowledge to human users. In this paper, we use genetic rule selection to decrease the number of extracted fuzzy rules. Through computational experiments, we examine the effect of genetic rule selection. First we extract fuzzy rules that satisfy minimum support and confidence levels. Thousands of fuzzy rules are extracted from numerical data in a heuristic manner. Then we apply genetic rule selection to extracted fuzzy rules. Experimental results show that genetic rule selection significantly decreases the number of extracted fuzzy rules without degrading their classification accuracy","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125653562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251167
P. Angelov, Xiaowei Zhou
Prediction of the properties of the crude oil distillation sidestreams based on statistical methods and laboratory-based analysis has been around for decades. However, there are still many problems with the existing estimators that require a development of new techniques especially for an on-line analysis of the quality of the distillation process. The nature of non-linear characteristics of the refinery process, the variety of properties to measure and control and the narrow window that normally refinery processes operate in are only some of the problems that a prediction technique should deal with in order to be useful for a practical application. There are many successful application cases that refinery units use real plant data to calibrate models. They can be used to predict quality properties of the gas oil, naphtha, kerosene and other products of a crude oil distillation tower. Some of these are distillation end points and cold properties (freeze, cloud). However, it is difficult to identify, control or compensate the dynamic process behavior and the errors from instrumentation for an online model prediction. The objective of this paper is to report an application and a study of a novel technique for real-time modeling, namely extended evolving fuzzy Takagi-Sugeno models (exTS) for prediction and online monitoring of these properties of the refinery distillation process. The results illustrate the effectiveness of the proposed technique and it's potential. The limitations and future directions of research are also outlined
{"title":"A Method for Predicting Quality of the Crude Oil Distillation","authors":"P. Angelov, Xiaowei Zhou","doi":"10.1109/ISEFS.2006.251167","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251167","url":null,"abstract":"Prediction of the properties of the crude oil distillation sidestreams based on statistical methods and laboratory-based analysis has been around for decades. However, there are still many problems with the existing estimators that require a development of new techniques especially for an on-line analysis of the quality of the distillation process. The nature of non-linear characteristics of the refinery process, the variety of properties to measure and control and the narrow window that normally refinery processes operate in are only some of the problems that a prediction technique should deal with in order to be useful for a practical application. There are many successful application cases that refinery units use real plant data to calibrate models. They can be used to predict quality properties of the gas oil, naphtha, kerosene and other products of a crude oil distillation tower. Some of these are distillation end points and cold properties (freeze, cloud). However, it is difficult to identify, control or compensate the dynamic process behavior and the errors from instrumentation for an online model prediction. The objective of this paper is to report an application and a study of a novel technique for real-time modeling, namely extended evolving fuzzy Takagi-Sugeno models (exTS) for prediction and online monitoring of these properties of the refinery distillation process. The results illustrate the effectiveness of the proposed technique and it's potential. The limitations and future directions of research are also outlined","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128489644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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