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2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)最新文献

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Paired Comparisons Method for Solving Multi-Label Learning Problem 求解多标签学习问题的配对比较方法
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.54
M. Petrovskiy
{"title":"Paired Comparisons Method for Solving Multi-Label Learning Problem","authors":"M. Petrovskiy","doi":"10.1109/HIS.2006.54","DOIUrl":"https://doi.org/10.1109/HIS.2006.54","url":null,"abstract":"Multi-label classification problem is a further generalization of traditional multi-class learning problem. In multi-label case the classes are not mutually exclusive and any sample may belong to several classes at the same time. Such problems occur in many important applications (in bioinformatics, text categorization, intrusion detection, etc.). In this paper we propose a new method for solving multi-label learning problem, based on paired comparisons approach. In this method each pair of possibly overlapping classes is separated by two probabilistic binary classifiers, which isolate the overlapping and non-overlapping areas. Then individual probabilities generated by binary classifiers are combined together to estimate final class probabilities fitting extended Bradley-Terry model with ties. Experimental performance evaluation on well-known multi-label benchmark datasets has demonstrated the outstanding accuracy results of the proposed method.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122370320","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
RBF-Neural Network Adaptive PID Control for 3-Axis Stabilized Tracking System 三轴稳定跟踪系统的rbf神经网络自适应PID控制
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.58
Zhigang Liu, Junzheng Wang
{"title":"RBF-Neural Network Adaptive PID Control for 3-Axis Stabilized Tracking System","authors":"Zhigang Liu, Junzheng Wang","doi":"10.1109/HIS.2006.58","DOIUrl":"https://doi.org/10.1109/HIS.2006.58","url":null,"abstract":"The 3-axis stabilized tracking system is a vital part of the anti-aircraft system. To achieve the demand on swiftness, precision and stability, an adaptive PID control algorithm based on RBF-NN is introduced. In order to verify the feasibility of the method, several experiments were taken under the same conditions while using both the traditional PID and the adaptive RBF-NN PID. The steady state error is 0.003¿, and the maximum tracking error is 0.203¿ when the signal frequency is 0.1Hz and the amplitude is 20¿ .The results of experiments proved that the RBF-NN adaptive PID controller performs well in the actual 3-axis stabilized tracking control system.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128026043","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: 6
Learning Generalized Weighted Relevance Aggregation Operators Using Levenberg-Marquardt Method 基于Levenberg-Marquardt方法的广义加权关联聚合算子学习
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.40
B. Mendis, Tom Gedeon, L. Kóczy
{"title":"Learning Generalized Weighted Relevance Aggregation Operators Using Levenberg-Marquardt Method","authors":"B. Mendis, Tom Gedeon, L. Kóczy","doi":"10.1109/HIS.2006.40","DOIUrl":"https://doi.org/10.1109/HIS.2006.40","url":null,"abstract":"We previously introduced the generalized Weighted Relevance Aggregation Operators (WRAO) for hierarchical fuzzy signatures. WRAO enhances the ability of the fuzzy signature model to adapt to different applications and simplifies the learning of fuzzy signature models from data. In this paper we overcome the practical issues which occur when learning WRAO from data. This paper discuss an algorithm for learning WRAO using the Levenberg- Marquardt (LM) method, which is one of the most sophisticated and widely used gradient based optimization method. Also, this paper shows the successful results of applying the proposed algorithm to extract WRAO for two real world problems namely High Salary Selection and SARS Patient Classification.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114824601","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: 17
Two Variants of the Constructive Neural Network Tiling Algorithm 构造神经网络平铺算法的两种变体
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.74
J. R. Bertini, M. C. Nicoletti, Estevam Hruschka, A. Ramer
{"title":"Two Variants of the Constructive Neural Network Tiling Algorithm","authors":"J. R. Bertini, M. C. Nicoletti, Estevam Hruschka, A. Ramer","doi":"10.1109/HIS.2006.74","DOIUrl":"https://doi.org/10.1109/HIS.2006.74","url":null,"abstract":"Unlike conventional neural network (NN) algorithms that require the definition of the NN architecture before learning starts, constructive neural network (CoNN) algorithms enable the NN architecture to be constructed along with the learning process. CoNN algorithms are very dependent on the TLU training algorithm they employ. Generally in their original proposal CoNN algorithms use a Perceptron-based algorithm for training each individual node added to the network during the learning process. This paper proposes two hybrid variants of the CoNN algorithm known as Tiling, referred to as Tiling_V1 and Tiling_V2. The two variants differ from the original Tiling in respect to the algorithm they use for training individual TLUs added to the NN. The master neuron in each hidden layer constructed by Tiling_V1 can be trained either by PRM (Pocket with Ratchet Modification) or BCPMin (Barycentric Correction Procedure) while the auxiliary neurons are always trained using BCPMin. In Tiling_V2 the same algorithm used to train the master neuron of each hidden layer is also used to train the auxiliary neurons. Both variants as well as the original Tiling (using PRM or BCPMin) have been used in learning tasks involving 7 knowledge domains. In 6 out of 7 domains results obtained with one of the variants are in the top two best results.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114926648","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: 3
Towards Effective Unbiased Automated Feature Selection 迈向有效、无偏的自动特征选择
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.72
K. Iswandy, A. König
{"title":"Towards Effective Unbiased Automated Feature Selection","authors":"K. Iswandy, A. König","doi":"10.1109/HIS.2006.72","DOIUrl":"https://doi.org/10.1109/HIS.2006.72","url":null,"abstract":"The selection of relevant and non-redundant features or variables from a larger set is an ubiquitous problem in many disciplines. Numerous automated methods have been introduced, however, the important issue of selection stability is still largely uncovered. It can be observed, that small changes in the data can lead to dramatic changes in the selection. This compromises both statistical reliability and recognition rates as well as knowledge extraction. In our work, we pursue an approach employing data sampling techniques, e.g., leave-one-out method, and generate statistics of selection to determine a stability factor and identify stable features. In this paper, we introduce improved selection techniques from first and second order statistics and demonstrate their effectiveness for three benchmark problems of increasing complexity.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130327774","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
Advanced Meta-PSO 先进Meta-PSO
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.10
Christian Veenhuis
{"title":"Advanced Meta-PSO","authors":"Christian Veenhuis","doi":"10.1109/HIS.2006.10","DOIUrl":"https://doi.org/10.1109/HIS.2006.10","url":null,"abstract":"One issue in applying PSO is to find a good working set of parameters. The standard settings are often work sufficiently but don¿t exhaust the possibilities of PSO. This paper proposes an extended Meta-PSO approach to optimize the PSO parameters as well as the neighborhood topology for a given problem by PSO itself. It is applied to four typical benchmark functions known from literature. The good results indicate that PSO is capable of optimizing itself.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"49 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113991992","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
Emergence of Information Processor Using Real World--Real-Time Learning of Pursuit Problem 利用真实世界的信息处理器的出现——追踪问题的实时学习
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.23
H. Fujii, Kazuyuki Ito, A. Gofuku
{"title":"Emergence of Information Processor Using Real World--Real-Time Learning of Pursuit Problem","authors":"H. Fujii, Kazuyuki Ito, A. Gofuku","doi":"10.1109/HIS.2006.23","DOIUrl":"https://doi.org/10.1109/HIS.2006.23","url":null,"abstract":"Real-time reinforcement learning is difficult because number of trials is too much to complete learning within limited time. To solve the problem, we consider reduction of action-state space by information processor using real world without prior knowledge. We obtain the information processor in evolution by setting the fitness as ease of learning. As a typical example, we address pursuit problem in which dynamics is regarded. As a result, the processor has been obtained in evolution and agent has learned in real-time.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115046083","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
Multiclass SVM Model Selection Using Particle Swarm Optimization 基于粒子群优化的多类SVM模型选择
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.50
Bruno Feres de Souza, A. Carvalho, R. Calvo, R. P. Ishii
{"title":"Multiclass SVM Model Selection Using Particle Swarm Optimization","authors":"Bruno Feres de Souza, A. Carvalho, R. Calvo, R. P. Ishii","doi":"10.1109/HIS.2006.50","DOIUrl":"https://doi.org/10.1109/HIS.2006.50","url":null,"abstract":"Tuning SVM hyperparameters is an important step for achieving good classification performance. In the binary case, the model selection issue is well studied. For multiclass problems, it is harder to choose appropriate values for the base binary models of a decomposition scheme. In this paper, the authors employ Particle Swarm Optimization to perform a multiclass model selection, which optimizes the hyperparameters considering both local and globalmodels. Experiments conducted over 4 benchmark problems show promising results.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133534482","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: 68
State Based Control of Wastewater Treatment Plants--Evaluation of the Algorithm in a Simulation Study 污水处理厂基于状态的控制——仿真研究中算法的评价
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.68
A. Ebel, M. Bongards, S. McLoone
{"title":"State Based Control of Wastewater Treatment Plants--Evaluation of the Algorithm in a Simulation Study","authors":"A. Ebel, M. Bongards, S. McLoone","doi":"10.1109/HIS.2006.68","DOIUrl":"https://doi.org/10.1109/HIS.2006.68","url":null,"abstract":"This paper presents the design of a supervised controller with the primary objectives of improvement of process stability and reduction of operating costs. The controller uses a rule set for determination of the process state extracted from the results of a fuzzy c-means clustering algorithm. Additionally a finite state machine is used for the evaluation of identified states in order to check logical consistency and process behavior. The result of the state machine is a symbolic value which describes the condition of the process. Based on the condition-identification an appropriate control strategy is used. The supervised controller methodology is evaluated in a simulation study. A significant improvement of process stability and energy saving has been achieved.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128815284","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
Intrinsic Evolution of Predictable Behavior Evolvable Hardware in Dynamic Environment 动态环境下可预测行为的内在演化
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.36
Peter Tawdross, S. Lakshmanan, A. König
{"title":"Intrinsic Evolution of Predictable Behavior Evolvable Hardware in Dynamic Environment","authors":"Peter Tawdross, S. Lakshmanan, A. König","doi":"10.1109/HIS.2006.36","DOIUrl":"https://doi.org/10.1109/HIS.2006.36","url":null,"abstract":"Sensor electronics performance is susceptible to static and dynamic deviations. Even laser trimming still can¿t deal with all the deviations. Recently, analog reconfigurable electronics offers a solution to compensate these effects. The state of the art uses genetic algorithm (GA) to find an arbitrary topology to fulfill the given specifications, which can cause hardware with unpredictable behavior. In case of any environmental change, the state of the art starts the evolution from scratch. Considering the robustness of the reconfiguration approach, we used the particle swarm optimization (PSO) [13] as an alternative to GA for reconfiguration of programmable sensor electronics. In this paper, we extend our work to investigate the PSO methods for dynamic environment in which the hardware can track the environmental change without starting from scratch. We run the algorithm on a real hardware (intrinsic evolution). Our hardware was designed in such a way that its performance is predictable by employing standard circuit topologies.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123382033","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: 12
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