2006 IEEE Conference on Cybernetics and Intelligent Systems最新文献_第9页

An Intelligent Restoration Method for Impulse Noise Highly-Corrupted Images 一种脉冲噪声严重损坏图像的智能恢复方法

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252306

Thou-Ho Chen, Chao-Yu Chen, Tsong-Yi Chen, Ming-Kun Wu

引用次数: 6

On Approximating K-MPE of Bayesian Networks Using Genetic Algorithm 用遗传算法逼近贝叶斯网络的K-MPE

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252340

N. Sriwachirawat, S. Auwatanamongkol

引用次数: 7

Towards a Learning Automata Solution to the Multi-Constraint Partitioning Problem 多约束划分问题的学习自动机解

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252348

G. Horn, B. Oommen

{"title":"Towards a Learning Automata Solution to the Multi-Constraint Partitioning Problem","authors":"G. Horn, B. Oommen","doi":"10.1109/ICCIS.2006.252348","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252348","url":null,"abstract":"We consider the problem of partitioning a set of elements (or objects) into mutually exclusive classes (or groups), where elements which are \"similar\" to each other are, hopefully, located in the same class. This problem has been shown to be NP-hard, and the literature reports solutions in which the similarity constraint consists of a single index. For example, typical \"similarity\" conditions that have been used in the literature include those in which \"similar\" objects are accessed together, or when they communicate (as processes do) with each other. In this paper, we present the first reported solution to the case when the objects could be linked together in a multi-constraint manner, and indeed, visit the scenario when the constraints could, themselves, be contradictory. The solution we propose is based on the theory of estimator-based learning automata (LA), operating in non-stationary environments. Rather than use traditional estimates, we advocate the use of stochastic weak-estimates (B. J. Oommen and L. Rueda, 2006) and the specific digraph properties of the relations between the elements. Although the solutions proposed perform admirably when the number of elements is small, the simulated results demonstrate that the quality of the final solution decreases with the number of elements. Thus, although this is the first reported solution to the problem which incorporates specific digraph properties of the objects, the scalability of the solution remains open","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132310917","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: 7

LabVIEW Implementation of an Auto-tuning PID Regulator via Grey-predictor 基于灰色预测器的PID调节器自整定的LabVIEW实现

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252318

Chien-Ming Lee, Yao-Lun Liu, Hong-Wei Shieh, Chia-Chang Tong

{"title":"LabVIEW Implementation of an Auto-tuning PID Regulator via Grey-predictor","authors":"Chien-Ming Lee, Yao-Lun Liu, Hong-Wei Shieh, Chia-Chang Tong","doi":"10.1109/ICCIS.2006.252318","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252318","url":null,"abstract":"The purpose of this paper is to design and implement a grey prediction controller (GPC) via LabVIEW as a test platform. Grey prediction model GM(1,1) is used with the aid of first-order, digital low-pass alpha filter to refine the estimation of the system response in advance. The prediction is then utilized to modify the parameters of PID controller. Hence, an auto-tuning PID controller according to the forecasting of system response is achieved. LabVIEW software programming with a data acquisition card (model DAQPad-6015) from National Instruments Co. is chosen to provide a high-resolution, however, time-saving solution for developing this auto-tuning control system. One temperature regulation example is arranged and tested to confirm this auto-tuning controller scheme. Test results of this novel grey prediction controller are derived and compared with traditional PID controller. The Grey prediction controller is far better than PID controller in the prospect of both the transient response and steady state response. Best of all, this auto-tuning regulator eliminates the hassle of human interference","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"30 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124204126","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: 18

Sincerity and User Avatar Research Based on Binocular Vision in Virtual Reality 基于双目视觉的虚拟现实真诚度与用户化身研究

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252235

Guisheng Yin, Dongmei Yang, Qi Wen, Churong Lai, Jie Shen

引用次数: 1

Thermal Error Modeling of a Machining Center using Grey System Theory and HGA-Trained Neural Network 基于灰色系统理论和hga训练神经网络的加工中心热误差建模

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252298

Kun-Chieh Wang

引用次数: 25

An Auto-tuning PID Regulator Using Grey Predictor 基于灰色预测器的自整定PID调节器

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252317

Ching-Yi Hsu, Shuen-Jeng Lin, Wei-Liang Chien, Chia-Chang Tong

{"title":"An Auto-tuning PID Regulator Using Grey Predictor","authors":"Ching-Yi Hsu, Shuen-Jeng Lin, Wei-Liang Chien, Chia-Chang Tong","doi":"10.1109/ICCIS.2006.252317","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252317","url":null,"abstract":"It is the purpose of this paper to introduce the advantages of grey predictor controllers. We adopt grey prediction to obtain simple and effective estimated values, and, with the aid of first-order low-pass alpha filter, greatly improve the accuracy subsequently used in the prediction for system response. The result will be in turn used to predict error and furthermore automatically adjust the parametrical values of PID controller, and accordingly will be able to deal with the possible variation of system responses at the very first stage. It can not only actively promote the responses efficiency of transient response, but also passively prevent disturbance. As a matter of fact, the highest demand of \"plug in and play\" can be met without any need to adjust the parameter. This paper will give a detailed specification of the system structure, the design, and the concept, as well as prove the modulation function of grey predictor controllers in unit step response by means of Matlab program simulation and mathematical argumentation. In transient response, it will effectively fasten rising time, shorten settling time, and oppress overshoot; meanwhile, in steady state response, it is able to reduce steady state error to zero and achieve what traditional PID cannot perform","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117116865","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

A Learning Algorithm for Local Linear Neuro-fuzzy Models with Self-construction through Merge & Split 一种自构造局部线性神经模糊模型的合并分割学习算法

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252305

A.S. Jamab, Babak Nadjar Araabi

{"title":"A Learning Algorithm for Local Linear Neuro-fuzzy Models with Self-construction through Merge & Split","authors":"A.S. Jamab, Babak Nadjar Araabi","doi":"10.1109/ICCIS.2006.252305","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252305","url":null,"abstract":"A self-constructing version of locally linear model tree (LOLIMOT) algorithm for structure identification in neuro-fuzzy models is proposed in this paper. LOLIMOT is an incremental tree-construction learning algorithm that partitions the input space by axis-orthogonal splits. In each iteration, LOLIMOT splits a local model into two models in a way that a local classification error is minimized. As a result, during the training procedure some of the formerly made divisions may become suboptimal or even superfluous. In this paper, the LOLIMOT is improved in two ways: (1) the ability to merge previously divided local linear models is added, and (2) a simulated annealing stochastic decision process is responsible to select a local model for splitting. Comparing to the LOLIMOT, our proposed improved learning algorithm shows the ability to construct models with fewer number of rules at comparable modeling errors. Algorithms are compared through a case study of nonlinear function approximation. Obtained results demonstrate the better performance of modified method as compared to that of original form of the LOLIMOT algorithm","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124079208","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: 7

Solving Constrained Optimization Problems by the ε Constrained Particle Swarm Optimizer with Adaptive Velocity Limit Control ε约束粒子群优化器自适应速度限制控制求解约束优化问题

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252248

T. Takahama, S. Sakai

引用次数: 43

A Study on Automatic Recognition of Road Signs 道路标志自动识别技术研究

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252289

Y. Nguwi, A. Kouzani

引用次数: 48