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Learning and Nonlinear Models最新文献

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Uso de Redes Neurais Artificiais e Teoria de Conjuntos Aproximativos no Estudo de Padrões Climáticos Sazonais 利用人工神经网络和近似集理论研究季节气候模式
Learning and Nonlinear Models Pub Date : 2016-12-05 DOI: 10.21528/LNLM-vol7-no2-art5
Juliana A. Anochi, J. D. Silva
{"title":"Uso de Redes Neurais Artificiais e Teoria de Conjuntos Aproximativos no Estudo de Padrões Climáticos Sazonais","authors":"Juliana A. Anochi, J. D. Silva","doi":"10.21528/LNLM-vol7-no2-art5","DOIUrl":"https://doi.org/10.21528/LNLM-vol7-no2-art5","url":null,"abstract":"","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121398178","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
Rede Neural ARTMAP-FUZZY e Transformada Wavelet para Detecção e Classificação de Distúrbios de Tensão em Sistemas de Energia Elétrica 用于电力系统电压干扰检测和分类的ARTMAP模糊神经网络和小波变换
Learning and Nonlinear Models Pub Date : 2016-12-05 DOI: 10.21528/LNLM-VOL7-NO1-ART2
F. C. V. Malange, C. R. Minussi
{"title":"Rede Neural ARTMAP-FUZZY e Transformada Wavelet para Detecção e Classificação de Distúrbios de Tensão em Sistemas de Energia Elétrica","authors":"F. C. V. Malange, C. R. Minussi","doi":"10.21528/LNLM-VOL7-NO1-ART2","DOIUrl":"https://doi.org/10.21528/LNLM-VOL7-NO1-ART2","url":null,"abstract":"− Many efforts have been dispended to solve problems related to the Electrical Energy Quality, principally in automation of process and developing monitoring equipments that provide improvements in behavior and reliability of the electrical system. This paper presents an automatic identifier/classifier system for disturbances called Wavelet-ARTMAP-Fuzzy neural network. The basic structure of this neural network is composed of three modules: the anomaly (disturbance) detection module; the characteristics extraction module, where the wave forms are analyzed by calculating the Discrete Wavelet Transform, Multiresolution Analysis, and Entropy Norm; and the classification disturbance module which contains a Fuzzy ARTMAP neural network that shows what kind of anomaly of the signal. This study considers seven types of electrical signals, generated from the mathematical models, performing 2800 wave forms. Thus, the performance of this network in detecting and classifying correctly the several electrical disturbances was 100%, moreover the robust form and velocity in obtaining the results, allowing using in real time.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128000092","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
Complexidade computacional de um algoritmo competitivo aplicado ao projeto de quantizadores vetoriais 应用于矢量量化器设计的竞争算法的计算复杂性
Learning and Nonlinear Models Pub Date : 2016-09-21 DOI: 10.21528/LNLM-VOL2-NO1-ART4
F. Madeiro, W. Lopes, B. G. A. Neto, M. Alencar
{"title":"Complexidade computacional de um algoritmo competitivo aplicado ao projeto de quantizadores vetoriais","authors":"F. Madeiro, W. Lopes, B. G. A. Neto, M. Alencar","doi":"10.21528/LNLM-VOL2-NO1-ART4","DOIUrl":"https://doi.org/10.21528/LNLM-VOL2-NO1-ART4","url":null,"abstract":"In the present paper, the computational complexity of a competitive learning algorithm applied to vector quantization (VQ) codebook design is investigated. Analytical expressions (as a function of the codebook size, the dimension of the codevectors, the number of training vectors and the number of iterations performed) are derived for the number of operations (multiplications, divisions, additions, subtractions and comparisons) performed by the competitive algorithm. Analytical expressions are also derived for the tradional LBG (Linde-Buzo-Gray) algorithm. Regarding VQ codebook design for image coding, results show that the computational complexity of the competitive algorithm is lower than that of LBG.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122814019","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
An Unifying Neuronal Model for Normal and Abnormal Thinking 正常与异常思维的统一神经元模型
Learning and Nonlinear Models Pub Date : 2016-09-21 DOI: 10.21528/LNLM-VOL2-NO1-ART1
D. Q. Mendes, L. A. V. D. Carvalho, R. S. Wedemann
{"title":"An Unifying Neuronal Model for Normal and Abnormal Thinking","authors":"D. Q. Mendes, L. A. V. D. Carvalho, R. S. Wedemann","doi":"10.21528/LNLM-VOL2-NO1-ART1","DOIUrl":"https://doi.org/10.21528/LNLM-VOL2-NO1-ART1","url":null,"abstract":"Since little is still known about fundamental brain mechanisms associated to thought, its different manifestations are usually classified in an oversimplified way into normal and abnormal, like delusional and disorganized thought or creative thinking. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, and the existence of semantic maps in the human brain, we developed a self-organizing neural network model to unify different thought processes into a single neurocomputational substrate. We performed simulations varying dopaminergic modulation and observed the total patterns that emerged at the resulting semantic map, assuming that these correspond to thought. The model thus shows how normal and abnormal thinking are generated, and that there are no clear borders between their different manifestations. Actually, a continuum of different qualitative reasoning, ranging from delusion to disorganized thought, and passing through normal and creative thinking, seems to be more plausible.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122835425","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
Combining modulation diversity and index assignment to improve image VQ for a rayleigh fading channel 结合调制分集和索引分配提高瑞利衰落信道图像VQ
Learning and Nonlinear Models Pub Date : 2016-09-21 DOI: 10.21528/LNLM-VOL2-NO1-ART3
W. Lopes, F. Madeiro, B. Neto, M. Alencar
{"title":"Combining modulation diversity and index assignment to improve image VQ for a rayleigh fading channel","authors":"W. Lopes, F. Madeiro, B. Neto, M. Alencar","doi":"10.21528/LNLM-VOL2-NO1-ART3","DOIUrl":"https://doi.org/10.21528/LNLM-VOL2-NO1-ART3","url":null,"abstract":"Vector quantization (VQ) has been widely used in image coding systems. However, it is highly sensitive to channel errors, which may lead to very annoying blocking artifacts in the reconstructed images. In the present paper, modulation diversity (MD) is combined with index assignment (IA) by simulated annealing for improving image VQ for a Rayleigh fading channel: MD is used to reduce the bit error probability while IA is used as an attempt to reduce the visual impact of channel errors.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115506397","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
Uma Abordagem para a Caracterização do Cancelamento Eletivo de Contratos em Planos de Saúde Privados 描述私人健康计划中选择性合同取消的方法
Learning and Nonlinear Models Pub Date : 2016-06-01 DOI: 10.21528/LNLM-vol14-no2-art2
Jefferson Henrique Camelo, R. Rabelo, Erick Baptista Passos
{"title":"Uma Abordagem para a Caracterização do Cancelamento Eletivo de Contratos em Planos de Saúde Privados","authors":"Jefferson Henrique Camelo, R. Rabelo, Erick Baptista Passos","doi":"10.21528/LNLM-vol14-no2-art2","DOIUrl":"https://doi.org/10.21528/LNLM-vol14-no2-art2","url":null,"abstract":"Orientador: Prof. Dr. Erick Baptista Passos (IFPI). Examinador Externo: Prof. Dr. Ricardo Augusto Souza Fernandes (UFSCAR). Examinador Externo: Prof. Dr. Antonio Helson Mineiro Soares (USP). Examinador Interno: Prof. Dr. Pedro de Alcântara dos Santos Neto.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132907811","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
Classificação Neural De Sinais De Sonar Passivo Com Base Em Componentes Independentes 基于独立分量的被动声纳信号的神经分类
Learning and Nonlinear Models Pub Date : 2016-04-14 DOI: 10.21528/LNLM-VOL5-NO1-ART2
N. Moura, J. Seixas, W. S. Filho
{"title":"Classificação Neural De Sinais De Sonar Passivo Com Base Em Componentes Independentes","authors":"N. Moura, J. Seixas, W. S. Filho","doi":"10.21528/LNLM-VOL5-NO1-ART2","DOIUrl":"https://doi.org/10.21528/LNLM-VOL5-NO1-ART2","url":null,"abstract":"The estimation of the direction of arrival (DOA) of a ship by a submarine relies very much in its passive sonar system. The noise radiated by a contact is received by the sonar sensors, using a beamformer to determine the direction of the noise. Besides the DOA estimation it is also necessary to identify the contact’s class in a given direction of interest, performing the classification of ships. The objective of the work presented here is to implement a classification system, using neural networks, to indentify contacts after the estimation of the direction of arrival has been performed. The classifier operates over independent component extracted from a LOFAR analysis, which is realized in each direction to obtain the main features of the ship signals. Keywords— Passive Sonar, Direction of Arrival, LOFAR, Beamforming, Neural Networks, Independent Component Analysis. Resumo— A estimação da direção de chegada (DOA) de um navio por um submarino envolve o seu sistema de sonar passivo. O rúıdo irradiado pelo contato é recebido pelos sensores do sonar que realiza uma conformação de feixes (beamforming) para a determinação da direção deste rúıdo. Além da estimação da DOA, se faz necessário também a identificação do rúıdo proveniente da direção de interesse, para que possa ser feita uma classificação dos navios. O objetivo do trabalho apresentado aqui é implementar um sistema de classificação, usando redes neurais, para identificar contatos após a estimação da direção de chegada ter sido realizada. O classificador opera sobre componentes independentes extráıda de uma análise LOFAR, que é realizada em cada direção de interesse para obter as caracteŕısticas principais dos sinais dos navios. Keywords— Sonar Passivo, Direção de chegada, Conformação de Feixes,Redes Neurais, Análise de Componentes Independentes.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133390543","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
Identificação e Previsão de Séries Temporais Utilizando LS-SVM Otimizado pelo Algoritmo de Cardumes 利用鱼群算法优化的LS-SVM进行时间序列识别和预测
Learning and Nonlinear Models Pub Date : 2016-03-16 DOI: 10.21528/LNLM-VOL10-NO2-ART4
Leonardo Trigueiro dos Santos, Edgar Leite dos Santos Filho, Leandro dos Santos Coelho
{"title":"Identificação e Previsão de Séries Temporais Utilizando LS-SVM Otimizado pelo Algoritmo de Cardumes","authors":"Leonardo Trigueiro dos Santos, Edgar Leite dos Santos Filho, Leandro dos Santos Coelho","doi":"10.21528/LNLM-VOL10-NO2-ART4","DOIUrl":"https://doi.org/10.21528/LNLM-VOL10-NO2-ART4","url":null,"abstract":"","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"2001 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129562683","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
Inferência Bayesiana no Desenvolvimento de Previsores Neurais de Vazão Diária Utilizando Informações de Precipitação 利用降水信息开发日流量神经预测器的贝叶斯推理
Learning and Nonlinear Models Pub Date : 2016-03-16 DOI: 10.21528/LNLM-VOL10-NO3-ART2
Caio Monteiro Leocádio, V. Ferreira
{"title":"Inferência Bayesiana no Desenvolvimento de Previsores Neurais de Vazão Diária Utilizando Informações de Precipitação","authors":"Caio Monteiro Leocádio, V. Ferreira","doi":"10.21528/LNLM-VOL10-NO3-ART2","DOIUrl":"https://doi.org/10.21528/LNLM-VOL10-NO3-ART2","url":null,"abstract":"","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132391207","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
Monthly Electric Energy Demand Forecasting By Fuzzy Inference System 基于模糊推理系统的月电力需求预测
Learning and Nonlinear Models Pub Date : 2016-03-16 DOI: 10.21528/LNLM-VOL10-NO2-ART6
I. Luna, R. Ballini
{"title":"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System","authors":"I. Luna, R. Ballini","doi":"10.21528/LNLM-VOL10-NO2-ART6","DOIUrl":"https://doi.org/10.21528/LNLM-VOL10-NO2-ART6","url":null,"abstract":"Resumo –Este artigo apresenta os resultados de prospecção de um e doz e passos à frente da demanda mensal de energia elétrica de uma concessionária de energia pertencente a reg ião sudeste do Brasil. Neste trabalho a demanda de energia el étrica total é subdividida em três grupos de consumo: residencial, industrial e comercial. O modelo de previsão adotado é basea do m regras nebulosas do tipo Takagi-Sugeno (TS), sendo o número de regras obtido via algoritmo de agrupamento não supervisi onado Subtractive Clustering . Uma base de regras nebulosa é determinada para cada classe d e consumo e os parâmetros do sistema de inferência são ajustados usando o algoritmo de otimização d e maximização da verossimilhança. Como variáveis de entrad a são consideradas as observações de demanda em instantes anteri ores além de variáveis explicativas de natureza macroeconô mica. O desempenho do modelo é verificado por meio de medidas de erros calculadas dentro e fora da amostra e os resultados indicam que o sistema de inferência nebuloso atinge índices de desem p nho na ordem anual de 3% para as classes de consumo.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129511132","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
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