2006 International Symposium on Evolving Fuzzy Systems最新文献_第6页

A Method for Predicting Quality of the Crude Oil Distillation 一种原油蒸馏质量预测方法

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251167

P. Angelov, Xiaowei Zhou

{"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":"26 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":"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}

引用次数: 24

Non-Parametric Model Structure Identification and Parametric Efficiency in Nonlinear State Dependent Parameter Models 非线性状态依赖参数模型的非参数模型结构辨识与参数效率

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251137

P. Young

引用次数: 7

Novelty Detection Based Machine Health Prognostics 基于机器健康预测的新颖性检测

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251161

Dimitar Filev, F. Tseng

引用次数: 49

Evolving Fuzzy Systems from Data Streams in Real-Time 实时数据流演化模糊系统

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-09-07 DOI: 10.1109/ISEFS.2006.251157

P. Angelov, Xiaowei Zhou

{"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}

引用次数: 243