2008 7th IEEE International Conference on Cybernetic Intelligent Systems最新文献_第6页

Modeling and reasoning intentional changes and their effects in social-technical systems with a goal-oriented approach 用目标导向的方法对社会技术系统中的有意变化及其影响进行建模和推理

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798977

Yong Du

引用次数: 1

A hybrid case-based rule-based system used in a content-based image retrieval system 一种用于基于内容的图像检索系统的基于案例的基于规则的混合系统

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798971

I. Bradford, C. Evans, R. Winter

引用次数: 0

Implementation of finite state automata using fLIF neurons 利用fLIF神经元实现有限状态自动机

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798918

Yulei Fan, C. Huyck

引用次数: 8

Neural network based adaptive nonlinear model inversion control of a twin rotor system in real time 基于神经网络的双转子系统自适应非线性模型实时反演控制

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798952

A. Rahideh, M. Shaheed, A. Bajodah

引用次数: 5

Cooperative co-evolution for school timetabling problem 学校排课问题的合作协同进化

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798942

M. S. Mohammadi, Caro Lucas

引用次数: 5

MLP and Elman recurrent neural network modelling for the TRMS TRMS的MLP和Elman递归神经网络建模

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798969

S. Toha, M. O. Tokhi

引用次数: 33

Optimizing twins decision tree classification, using genetic algorithms 优化双胞胎决策树分类，使用遗传算法

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798957

F. Seifi, M. Kangavari, H. Ahmadi, E. Lotfi, S. Imaniyan, S. Lagzian

{"title":"Optimizing twins decision tree classification, using genetic algorithms","authors":"F. Seifi, M. Kangavari, H. Ahmadi, E. Lotfi, S. Imaniyan, S. Lagzian","doi":"10.1109/UKRICIS.2008.4798957","DOIUrl":"https://doi.org/10.1109/UKRICIS.2008.4798957","url":null,"abstract":"Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been invented to construct decision tree classifiers. These approaches try to optimize parameters such as accuracy, speed of classification, size of constructed trees, learning speed, and the amount of used memory. There is a trade off between these parameters. That is to say that optimization of one may cause obstruction in the other, hence all existing approaches try to establish equilibrium. In this study, considering the effect of the whole data set on class assigning of any data, we propose a new approach to construct not perfectly accurate, but less complex trees in a short time, using small amount of memory. To achieve this purpose, a multi-step process has been used. We trace the training data set twice in any step, from the beginning to the end and vice versa, to extract the class pattern for attribute selection. Using the selected attribute, we make new branches in the tree. After making branches, the selected attribute and some records of training data set are deleted at the end of any step. This process continues alternatively in several steps for remaining data and attributes until the tree is completely constructed. In order to have an optimized tree the parameters which we use in this algorithm are optimized using genetic algorithms. In order to compare this new approach with previous ones we used some known data sets which have been used in different researches. This approach has been compared with others based on the classification accuracy and also the decision tree size. Experimental results show that it is efficient to use this approach particularly in cases of massive data sets, memory restrictions or short learning time.","PeriodicalId":311925,"journal":{"name":"2008 7th IEEE International Conference on Cybernetic Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129737055","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: 4

Sensor fusion in Robot localization using DS-Evidence Theory with conflict detection using Mahalanobis distance 基于ds证据理论的机器人定位传感器融合与基于马氏距离的冲突检测

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 2008-09-01 DOI: 10.1109/UKRICIS.2008.4798964

S. Soleimanpour, S. S. Ghidary, Kourosh Meshgi

引用次数: 7

Eyes extraction from facial images using edge density 基于边缘密度的人脸图像眼睛提取

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 1900-01-01 DOI: 10.1109/UKRICIS.2008.4798958

M. Shafi, P. Chung

引用次数: 10

Fuzzy rule base assessment models: Theoretical analyses and a case study 模糊规则基评价模型:理论分析与案例研究

2008 7th IEEE International Conference on Cybernetic Intelligent Systems Pub Date : 1900-01-01 DOI: 10.1109/UKRICIS.2008.4798968

K. Tay, C. Lim, C. S. Teh

{"title":"Fuzzy rule base assessment models: Theoretical analyses and a case study","authors":"K. Tay, C. Lim, C. S. Teh","doi":"10.1109/UKRICIS.2008.4798968","DOIUrl":"https://doi.org/10.1109/UKRICIS.2008.4798968","url":null,"abstract":"An assessment model is usually a mathematical model that produces a measuring index, in the form of a numerical score to a situation/object, with respect to the subject of measure. To allow a valid and useful comparison among various situations/objects according to their associated numerical scores to be made, two important properties, i.e., the monotone output property and output resolution properties, are essential in fuzzy inference-based assessment problems. In this paper, the conditions for a fuzzy assessment model to fulfill the monotone output property is investigated using a derivative approach. A guideline on how the input membership functions should be tuned is also provided. Besides, the output resolution property is defined as the derivative of the output of the assessment model with respect to the input, whereby the derivative should be greater than a minimum resolution. Based on the derivative, improvements to the output resolution property by refining the fuzzy production rules are suggested. A case study on the Bowles fuzzy RPN model to demonstrate the effectiveness of the properties is also included.","PeriodicalId":311925,"journal":{"name":"2008 7th IEEE International Conference on Cybernetic Intelligent Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122655473","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: 0