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
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Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251137
P. Young
Although neuro-fuzzy models provide a very useful general approach to the data-based modelling of nonlinear systems, their normal 'black box' nature is often a deterrent to their use in many of the natural sciences, where representation in terms of differential equations, or equivalent difference equations, is normally required and where the internal functioning and physical meaning of the model system is an important aspect of the modelling exercise. Moreover, identification of the model's internal structure can lead to considerable simplification of the model and the avoidance of over-parameterization, with important consequences as regards the statistical efficiency of the model parameter estimates. This paper introduces a non-parametric approach to model structure identification, based on recursive fixed interval smoothing, and shows how it can prove advantageous in the final parametric modelling of stochastic dynamic systems
{"title":"Non-Parametric Model Structure Identification and Parametric Efficiency in Nonlinear State Dependent Parameter Models","authors":"P. Young","doi":"10.1109/ISEFS.2006.251137","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251137","url":null,"abstract":"Although neuro-fuzzy models provide a very useful general approach to the data-based modelling of nonlinear systems, their normal 'black box' nature is often a deterrent to their use in many of the natural sciences, where representation in terms of differential equations, or equivalent difference equations, is normally required and where the internal functioning and physical meaning of the model system is an important aspect of the modelling exercise. Moreover, identification of the model's internal structure can lead to considerable simplification of the model and the avoidance of over-parameterization, with important consequences as regards the statistical efficiency of the model parameter estimates. This paper introduces a non-parametric approach to model structure identification, based on recursive fixed interval smoothing, and shows how it can prove advantageous in the final parametric modelling of stochastic dynamic systems","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021944","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.2006.251161
Dimitar Filev, F. Tseng
In this paper we present a new novelty detection algorithm for continuous real time monitoring of machine health and prediction of potential machine faults. The kernel of the system is a generic evolving model that is not dependent on the specific measured parameters determining the health of a particular machine. Two alternative strategies are introduced in order to predict abrupt and gradually developing (incipient) changes. This algorithm is realized as an autonomous software agent that continuously updates its decision model implementing an unsupervisory recursive learning algorithm. Results of validation of the proposed algorithm by accelerated testing experiments are also discussed
{"title":"Novelty Detection Based Machine Health Prognostics","authors":"Dimitar Filev, F. Tseng","doi":"10.1109/ISEFS.2006.251161","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251161","url":null,"abstract":"In this paper we present a new novelty detection algorithm for continuous real time monitoring of machine health and prediction of potential machine faults. The kernel of the system is a generic evolving model that is not dependent on the specific measured parameters determining the health of a particular machine. Two alternative strategies are introduced in order to predict abrupt and gradually developing (incipient) changes. This algorithm is realized as an autonomous software agent that continuously updates its decision model implementing an unsupervisory recursive learning algorithm. Results of validation of the proposed algorithm by accelerated testing experiments are also discussed","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114933519","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 : 2006-09-07DOI: 10.1109/ISEFS.2006.251157
P. Angelov, Xiaowei Zhou
An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper. The xTS fuzzy system combines both zero and first order Takagi-Sugeno (TS) type systems. The fuzzy rule-base (system structure) evolves starting 'from scratch' based on the data distribution in the joint input/output data space. An incremental clustering procedure that takes into account the non-stationary nature of the data pattern and generates clusters that are used to form fuzzy rule based systems antecedent part in on-line mode is used as a first stage of the non-iterative learning process. This structure proved to be computationally efficient and powerful to represent in a transparent way complex non-linear relationships. The decoupling of the learning task into a non-iterative, recursive (thus computationally very efficient and applicable in real-time) clustering with a modified version of the well known recursive parameter estimation technique leads to a very powerful construct - evolving xTS (exTS). It is transparent and linguistically interpretable. The contributions of this paper are: i) introduction of an adaptive recursively updated radius of the clusters (zone of influence of the fuzzy rules) that learns the data distribution/variance/scatter in each cluster; ii) a new condition to replace clusters that excludes contradictory rules; iii) an extended formulation that includes both zero order TS and simplified Mamdani multi-input-multi-output (MIMO) systems; iv) new improved formulation of the membership functions, which closer resembles the normal Gaussian distribution; v) introduction of measures of clusters quality that are used to form the antecedent parts of respective fuzzy rules, namely their age and support; vi) experimental results with a well known benchmark problem as well as with real experimental data of concentration of exhaust gases (NOx) in on-line modeling of car engine test rigs
{"title":"Evolving Fuzzy Systems from Data Streams in Real-Time","authors":"P. Angelov, Xiaowei Zhou","doi":"10.1109/ISEFS.2006.251157","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251157","url":null,"abstract":"An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper. The xTS fuzzy system combines both zero and first order Takagi-Sugeno (TS) type systems. The fuzzy rule-base (system structure) evolves starting 'from scratch' based on the data distribution in the joint input/output data space. An incremental clustering procedure that takes into account the non-stationary nature of the data pattern and generates clusters that are used to form fuzzy rule based systems antecedent part in on-line mode is used as a first stage of the non-iterative learning process. This structure proved to be computationally efficient and powerful to represent in a transparent way complex non-linear relationships. The decoupling of the learning task into a non-iterative, recursive (thus computationally very efficient and applicable in real-time) clustering with a modified version of the well known recursive parameter estimation technique leads to a very powerful construct - evolving xTS (exTS). It is transparent and linguistically interpretable. The contributions of this paper are: i) introduction of an adaptive recursively updated radius of the clusters (zone of influence of the fuzzy rules) that learns the data distribution/variance/scatter in each cluster; ii) a new condition to replace clusters that excludes contradictory rules; iii) an extended formulation that includes both zero order TS and simplified Mamdani multi-input-multi-output (MIMO) systems; iv) new improved formulation of the membership functions, which closer resembles the normal Gaussian distribution; v) introduction of measures of clusters quality that are used to form the antecedent parts of respective fuzzy rules, namely their age and support; vi) experimental results with a well known benchmark problem as well as with real experimental data of concentration of exhaust gases (NOx) in on-line modeling of car engine test rigs","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124192699","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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