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2010 UK Workshop on Computational Intelligence (UKCI)最新文献

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Tuning fuzzy systems by simulated annealing to predict time series with added noise 用模拟退火对模糊系统进行整定以预测有附加噪声的时间序列
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625596
Majid Almaraashi, R. John
{"title":"Tuning fuzzy systems by simulated annealing to predict time series with added noise","authors":"Majid Almaraashi, R. John","doi":"10.1109/UKCI.2010.5625596","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625596","url":null,"abstract":"In this paper, a combination of fuzzy system models and simulated annealing are used to predict Mackey-Glass time series with different levels of added noise by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules under singleton and non-singleton fuzzifications for both Mamdani and Takagi-Sugeno (TSK). The results of the proposed methods are compared by their ability to handle uncertainty.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130084449","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: 10
The application of colour FIRE to robot vision 彩色FIRE在机器人视觉中的应用
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625568
David Croft, S. Coupland
{"title":"The application of colour FIRE to robot vision","authors":"David Croft, S. Coupland","doi":"10.1109/UKCI.2010.5625568","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625568","url":null,"abstract":"This paper presents a modified FIRE algorithm capable of performing robust edge detection on full colour images. The colour FIRE algorithm is demonstrated on the well known Lena image and comparisons are made with existing edge detection methods. The main application of the colour FIRE presented here is that of robot vision, in particular the application to robot design for the puck collect competition held annually in Vienna, Austria. We show the colour FIRE algorithm to be useful in a robot vision system, giving a good rate of puck classification whist achieving a very low false positive rate.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123433562","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: 1
Authorship Attribution in Arabic using a hybrid of evolutionary search and linear discriminant analysis 作者归属在阿拉伯语使用混合进化搜索和线性判别分析
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625580
Kareem Shaker, D. Corne
{"title":"Authorship Attribution in Arabic using a hybrid of evolutionary search and linear discriminant analysis","authors":"Kareem Shaker, D. Corne","doi":"10.1109/UKCI.2010.5625580","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625580","url":null,"abstract":"Authorship Attribution is the problem of determining the authorship of one or more texts. Applications include disputed authorship, or deciding which of a collection of pieces of text were by the same author. A popular and successful approach is to characterize a specific author in terms of the usage pattern of function words. These are common words that are unrelated to subject matter, and tend to be used in specific ways by different authors. In English, a well-known collection of 70 function words is often used for this purpose. Previously, using a hybrid of evolutionary search and linear-discriminant analysis (LDA), we have shown excellent performance in authorship attribution in English based on a function word approach. Here, for the first time, we propose and test a set of Arabic function words for use in Arabic authorship attribution. Tests indicate that the chosen collection forms an effective basis for authorship attribution in Arabic.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128804965","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: 32
Fuzzy data fusion for fault detection in Wireless Sensor Networks 基于模糊数据融合的无线传感器网络故障检测
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625598
J. Shell, S. Coupland, E. Goodyer
{"title":"Fuzzy data fusion for fault detection in Wireless Sensor Networks","authors":"J. Shell, S. Coupland, E. Goodyer","doi":"10.1109/UKCI.2010.5625598","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625598","url":null,"abstract":"Wireless Sensor Networks (WSN) can produce decisions that are unreliable due to the large inherent uncertainties in the areas which they are deployed. It is vital for the applications where WSN's are deployed that accurate decisions can be made from the data produced. Fault detection is a vital pursuit, however it is a challenging task. In this paper we present a fuzzy logic data fusion approach to fault detection within a Wireless Sensor Network using a Statistical Process Control and a clustered covariance method. Through the use of a fuzzy logic data fusion approach we have introduced a novel technique into this area to reduce uncertainty and false-positives within the fault detection process.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"75 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133136349","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: 34
Moving from data to text using causal statements in explanatory narratives 在解释性叙述中使用因果陈述从数据转移到文本
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625591
Donald Matheson, Somayujulu Sripada, G. Coghill
{"title":"Moving from data to text using causal statements in explanatory narratives","authors":"Donald Matheson, Somayujulu Sripada, G. Coghill","doi":"10.1109/UKCI.2010.5625591","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625591","url":null,"abstract":"Data-to-text natural language generation techniques do not currently impart deep meaning in their output and leave it to an expert user to draw causal inferences. Frequently, the expert is adding meaning that would be present in data sources that could be made available to the NLG system. As the system is intended to convey as much information as possible, it seems counterintuitive to require the user to add meaning that could already have been included in the systems output. In this paper, we introduce our concept of using a reasoning engine to draw causal inferences about the data and then expressing them in an explanatory narrative.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116544051","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: 1
Testing the Dinosaur Hypothesis under different GP algorithms 在不同GP算法下检验恐龙假说
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625593
Michael Kampouridis, Shu-Heng Chen, E. Tsang
{"title":"Testing the Dinosaur Hypothesis under different GP algorithms","authors":"Michael Kampouridis, Shu-Heng Chen, E. Tsang","doi":"10.1109/UKCI.2010.5625593","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625593","url":null,"abstract":"The Dinosaur Hypothesis states that the behaviour of a market never settles down and that the population of predictors continually co-evolves with this market. This observation had been made and tested under artificial datasets. Recently, we formalized this hypothesis and also tested it under 10 empirical datasets. The tests were based on a GP system. However, it could be argued that results are dependent on the GP algorithm. In this paper, we test the Dinosaur Hypothesis under two different GP algorithms, in order to prove that the previous results are rigorous and are not sensitive to the choice of GP. We thus test again the hypothesis under the same 10 empirical datasets. Results are consistent among all three algorithms and thus suggest that market behavior can actually repeat itself, and have a number of ‘typical states’, where past rules may become useful again.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123975153","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: 1
MOEA/D for constrained multiobjective optimization: Some preliminary experimental results 约束多目标优化的MOEA/D:一些初步实验结果
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625585
Muhammad Asif Jan, Qingfu Zhang
{"title":"MOEA/D for constrained multiobjective optimization: Some preliminary experimental results","authors":"Muhammad Asif Jan, Qingfu Zhang","doi":"10.1109/UKCI.2010.5625585","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625585","url":null,"abstract":"This paper modifies the replacement and update scheme in MOEA/D-DE developed in [1] for dealing with constraints in multiobjective optimization problems. The modified scheme introduces a penalty function to penalize infeasible solutions. The penalty function uses a threshold to control the amount of penalty to infeasible solutions. Experimental results have shown that this penalty method is very promising.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126565113","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: 82
A PRESS statistic for two-block partial least squares regression 二块偏最小二乘回归的PRESS统计
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625583
B. McWilliams, G. Montana
{"title":"A PRESS statistic for two-block partial least squares regression","authors":"B. McWilliams, G. Montana","doi":"10.1109/UKCI.2010.5625583","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625583","url":null,"abstract":"Predictive modelling of multivariate data where both the covariates and responses are high-dimensional is becoming an increasingly popular task in many data mining applications. Partial Least Squares (PLS) regression often turns out to be a useful model in these situations since it performs dimensionality reduction by assuming the existence of a small number of latent factors that may explain the linear dependence between input and output. In practice, the number of latent factors to be retained, which controls the complexity of the model and its predictive ability, has to be carefully selected. Typically this is done by cross validating a performance measure, such as the predictive error. Although cross validation works well in many practical settings, it can be computationally expensive. Various extensions to PLS have also been proposed for regularising the PLS solution and performing simultaneous dimensionality reduction and variable selection, but these come at the expense of additional complexity parameters that also need to be tuned by cross-validation. In this paper we derive a computationally efficient alternative to leave-one-out cross validation (LOOCV), a predicted sum of squares (PRESS) statistic for two-block PLS. We show that the PRESS is nearly identical to LOOCV but has the computational expense of only a single PLS model fit. Examples of the PRESS for selecting the number of latent factors and regularisation parameters are provided.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930173","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: 8
Temporal difference learning with Interpolated N-Tuple networks: initial results on pole balancing 内插n元组网络的时间差分学习:极点平衡的初步结果
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625609
Aisha A. Abdullahi, S. Lucas
{"title":"Temporal difference learning with Interpolated N-Tuple networks: initial results on pole balancing","authors":"Aisha A. Abdullahi, S. Lucas","doi":"10.1109/UKCI.2010.5625609","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625609","url":null,"abstract":"Temporal difference learning (TDL) is perhaps the most widely used reinforcement learning method and gives competitive results on a range of problems, especially when using linear or table-based function approximators. However, it has been shown to give poor results on some continuous control problems and an important question is how it can be applied to such problems more effectively. The crucial point is how TDL can be generalized and scaled to deal with complex, high-dimensional problems without suffering from the curse of dimensionality. We introduce a new function approximation architecture called the Interpolated N-Tuple network and perform a proof-of-concept test on a classic reinforcement learning problem of pole balancing. The results show the method to be highly effective on this problem. They offer an important counter-example to some recently reported results that showed neuro-evolution outperforming TDL. The TDL with Interpolated N-Tuple networks learns to balance the pole considerably faster than the leading neuro-evolution techniques.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114062940","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: 2
Multi-objective evolution for Car Setup Optimization 汽车配置优化的多目标进化
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625607
Jorge Muñoz, G. Gutiérrez, A. Sanchis
{"title":"Multi-objective evolution for Car Setup Optimization","authors":"Jorge Muñoz, G. Gutiérrez, A. Sanchis","doi":"10.1109/UKCI.2010.5625607","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625607","url":null,"abstract":"This paper describes the winner algorithm of the Car Setup Optimization Competition that took place in EvoStar (2010). The aim of this competition is to create an optimization algorithm to fine tune the parameters of a car in the The Open Racing Car Simulator (TORCS) video game. There were five participants of the competition plus the two algorithms presented by the organizers (that do not take part in the competition). Our algorithm is a Multi-Objective Evolutionary Algorithm (MOEA) based on the Non-Dominated Sorting Genetic Algorithm (NSGAII) adapted to the constraints of the competition, that focus its fitness function in the lap time. Our results are also compared with other evolutionary algorithms and with the results of the other competition participants.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123958974","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: 13
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