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IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)最新文献

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Olfactory signal classification based on evolutionary computation 基于进化计算的嗅觉信号分类
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.831509
D. Dumitrescu, B. Lazzerini, F. Marcelloni
{"title":"Olfactory signal classification based on evolutionary computation","authors":"D. Dumitrescu, B. Lazzerini, F. Marcelloni","doi":"10.1109/IJCNN.1999.831509","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.831509","url":null,"abstract":"In this paper, we propose an evolutionary method for detecting the optimal number of clusters in a data set, and describe its application to classification of signals generated by olfactory sensors. The method is based on a new evolutionary search and optimization strategy. The strategy forces the formation and maintenance of subpopulations of solutions. Subpopulations co-evolve and converge towards different (sub-)optimal problem solutions. Only local chromosome interactions are allowed in order to avoid migration between subpopulations approximating different optimum points and to prevent the destruction of subpopulations. To this aim, specific selection and acceptance strategies have been defined. Experimental results obtained by applying the method to two test cases are also included.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061903","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
Real-time neurofuzzy control for an underactuated robot 欠驱动机器人的实时神经模糊控制
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.833406
F. Lara-Rojo, E. Sánchez, E. V. C. Jiménez
{"title":"Real-time neurofuzzy control for an underactuated robot","authors":"F. Lara-Rojo, E. Sánchez, E. V. C. Jiménez","doi":"10.1109/IJCNN.1999.833406","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.833406","url":null,"abstract":"We use a neurofuzzy approach, the NEFCON model, to generate and optimize a fuzzy controller for real-time control of an underactuated robot: the Pendubot, which consists of a two link inverted pendulum actuated only at the first join. The NEFCON learning algorithm is able to learn fuzzy rules as well as fuzzy sets. We present the results of the learning process for a fuzzy controller to balance the Pendubot in its highest inverted position, simulation results, and real-time results. The extension of this work to include the learning process of a swing-up procedure is in progress.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122342254","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
Application of Bayesian neural network in electrical impedance tomography 贝叶斯神经网络在电阻抗断层成像中的应用
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.830787
J. Lampinen, Aki Vehtari, K. Leinonen
{"title":"Application of Bayesian neural network in electrical impedance tomography","authors":"J. Lampinen, Aki Vehtari, K. Leinonen","doi":"10.1109/IJCNN.1999.830787","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.830787","url":null,"abstract":"In this contribution we present a method for solving the inverse problem in electric impedance tomography with neural networks. The problem of reconstructing the conductivity distribution inside an object from potential measurements on the surface is known to be ill-posed requiring efficient regularization techniques. We demonstrate that a statistical inverse solution, where the mean of the inverse mapping is approximated with a neural network gives promising results. We study the effect of input and output data representation by simulations and conclude that projection to principal axis is feasible data transformation. Also we demonstrate that Bayesian neural networks, which aim to average over all network models weighted by the model's posterior probability provide the best reconstruction results. With the presented approach estimation of some target variables, such as the void fraction (the ratio of gas and liquid), may be applicable directly without the actual image reconstruction. We also demonstrate that the solutions are very robust against noise in inputs.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122612693","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
A neural network for learning domain rules with precision 一种用于精确学习领域规则的神经网络
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.831136
L. Fu
{"title":"A neural network for learning domain rules with precision","authors":"L. Fu","doi":"10.1109/IJCNN.1999.831136","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.831136","url":null,"abstract":"To discover underlying domain regularities or rules has been a major long-term goal for scientific research (knowledge discovery) and engineering application (problem solving). However, when the domain rules get complex, current machine learning programs learn only approximate rather than true domain rules from a limited amount of observed data. This paper presents a new neural-network-based system which is intended for discovering precisely the domain rules with neither false positives nor false negatives. In a performance study, this system is ten times more accurate than the most well-known rule-learning system, C4.5, in terms of the rate of false rules induced from the data.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122824827","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
A non-feedback neuron filter algorithm for separated board-level routing problems in FPGA-based logic emulation systems 基于fpga的逻辑仿真系统中分离板级路由问题的非反馈神经元滤波算法
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.836197
Y. Takenaka, N. Funabiki
{"title":"A non-feedback neuron filter algorithm for separated board-level routing problems in FPGA-based logic emulation systems","authors":"Y. Takenaka, N. Funabiki","doi":"10.1109/IJCNN.1999.836197","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.836197","url":null,"abstract":"This paper presents a neuron filter algorithm to satisfy two constraints of the graph-coloring problem through a separated board-level routing problem (s-BLRP) in an FPGA-based logic emulation system. For a rapid prototyping of large scale digital systems, multiple FPGAs provide an efficient logic emulation system, where signals or nets between design partitions embedded on different FPGAs are connected through crossbars. We propose a new neuron filter algorithm to satisfy the two constraints of the problem simultaneously. The simulation results in randomly generated benchmark site instances show that our neuron filter algorithm with the thinning out application provides the better routing capability with the shorter computation time.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122929269","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
Multi-step-ahead prediction using dynamic recurrent neural networks 基于动态递归神经网络的多步超前预测
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.831517
A. Parlos, Omar T. Rais, A. Atiya
{"title":"Multi-step-ahead prediction using dynamic recurrent neural networks","authors":"A. Parlos, Omar T. Rais, A. Atiya","doi":"10.1109/IJCNN.1999.831517","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.831517","url":null,"abstract":"In numerous problems, such as in process control utilizing predictive control algorithms, it is required that a variable of interest be predicted multiple time-steps ahead into the future without having measurements of that variable in the horizon of interest. Additionally, in applications involving forecasting and fault diagnosis the availability of multistep-ahead predictors (MSP) is desired. MSPs are difficult to design because lack of measurements in the prediction horizon necessitates the recursive use of single-step-ahead predictors for reaching the final point in the horizon. Even small prediction errors resulting from noise at each point in the horizon accumulate and propagate, often resulting in poor prediction accuracy. We present a method for designing MSP using dynamic recurrent neural networks. The method is based on a dynamic gradient descent learning algorithm and its effectiveness is demonstrated through applications to an open-loop unstable process system, namely a heat-exchanger.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"349 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114049335","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: 180
Neural network methods for rule induction 规则归纳的神经网络方法
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.830845
R. Silva, Teresa B Ludermir
{"title":"Neural network methods for rule induction","authors":"R. Silva, Teresa B Ludermir","doi":"10.1109/IJCNN.1999.830845","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.830845","url":null,"abstract":"Local basis function networks are a useful category of classifiers, with known variations developed in neural networks, machine learning and statistics communities. The localized range of activation of the hidden units have many similarities with rule-based representations. Neurofuzzy systems are a common example of a framework that explicitly integrates these approaches. Following this concept, we study alternatives for the development of hybrid rule-neural systems with the purpose of inducing robust and interpretable classifiers. Local fitting of parameters is done by a gradient descent optimization that modifies the covering produced by a rule induction algorithm. Two tasks are accomplished: how to select a small number of rules and how to improve precision. The use of this architecture is better suited when one wants to achieve a good compromise between classification performance and simplicity.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114517243","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 game-theoretic formulation on adaptive categorization in ART networks ART网络自适应分类的博弈论表述
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.831106
W. Fung, Y. Liu
{"title":"A game-theoretic formulation on adaptive categorization in ART networks","authors":"W. Fung, Y. Liu","doi":"10.1109/IJCNN.1999.831106","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.831106","url":null,"abstract":"The concept of adaptive categorization is introduced to ART-type networks in this paper. Adaptive categorization capability also improves learning performance in self-organizing systems and online learning systems. Classical ART-types networks, however, have only fixed single size cluster formation in categorization, which is controlled by the scalar vigilance parameter. This categorization methodology usually cannot give satisfactory results as the data pattern space is not covered thoroughly by fixed boundary clusters. A game-theoretic formulation and analysis on the competitive clustering nature of ART-type networks are presented. A game-theoretic vigilance parameter adaptation algorithm is then proposed to form variable sized clusters so that the data pattern space is covered much thoroughly. Simulations are presented to demonstrate reliable categorizations obtained from variable sized clusters using game-theoretic vigilance parameter adaptation.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121901668","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
Rule extraction from neural networks: modified RX algorithm 神经网络规则提取:改进的RX算法
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.833466
Eduardo R. Hruschka, N. Ebecken
{"title":"Rule extraction from neural networks: modified RX algorithm","authors":"Eduardo R. Hruschka, N. Ebecken","doi":"10.1109/IJCNN.1999.833466","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.833466","url":null,"abstract":"The main challenge in using supervised neural networks in data mining applications is to get explicit knowledge from these models. For this purpose, a study on knowledge acquisition from supervised neural networks employed for classification problems is presented. An algorithm for rule extraction from neural networks, based on the RX algorithm is developed. This algorithm, named modified RX, is experimentally evaluated in two different domains: Iris Plants Database and Pima Indians Diabetes Database. The results are compared to those obtained by classification trees. As far as the efficacy is concerned, one observes that the successful application of the algorithm mainly depends on the knowledge representation acquired by the connectionist model, whereas the efficiency only depends on the neural network training time.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116626087","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: 20
Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural network 神经网络活动神经元的多项式复杂度GMDH归纳整理算法
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI: 10.1109/IJCNN.1999.831124
A. Ivakhnenko, D. Wunsch, G. A. Ivakhnenko
{"title":"Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural network","authors":"A. Ivakhnenko, D. Wunsch, G. A. Ivakhnenko","doi":"10.1109/IJCNN.1999.831124","DOIUrl":"https://doi.org/10.1109/IJCNN.1999.831124","url":null,"abstract":"Neural networks with active neurons which self-organize their structure can use inductive sorting-out GMDH algorithms for their neurons. New threshold type GMDH algorithm with polynomial complexity is developed to decrease computing time in case of large input data sample.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116719449","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: 35
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