Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.94
Gabriela I. L. Alves, D. A. Silva, Emeson J. S. Pereira, T. Ferreira
Artificial Neural Networks (ANN) have been widely used in time series forecasting problem. However, a more promising approach is the combination of ANN with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc, where these evolutionary algorithms have the objective of train and adjust all parameter of the ANN. In the evolutionary process is necessary define a fitness function to guide the evolve process. Thus, for a set of possibles fitness function, how to determine the function more efficient? This paper aims to select the efficient fitness functions, through the use of Data Envelopment Analysis. This tool determines the relative efficiency of each unit under review, comparing it with each other and considering the relationship between inputs and outputs. Two different times series were used to benchmark the set of twenty fitness functions. The preliminary results show the proposed method is a promising approach for efficient selecting the fitness function.
{"title":"Data Envelopment Analysis for Selection of the Fitness Function in Evolutionary Algorithms Applied to Time Series Forecasting Problem","authors":"Gabriela I. L. Alves, D. A. Silva, Emeson J. S. Pereira, T. Ferreira","doi":"10.1109/BRICS-CCI-CBIC.2013.94","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.94","url":null,"abstract":"Artificial Neural Networks (ANN) have been widely used in time series forecasting problem. However, a more promising approach is the combination of ANN with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc, where these evolutionary algorithms have the objective of train and adjust all parameter of the ANN. In the evolutionary process is necessary define a fitness function to guide the evolve process. Thus, for a set of possibles fitness function, how to determine the function more efficient? This paper aims to select the efficient fitness functions, through the use of Data Envelopment Analysis. This tool determines the relative efficiency of each unit under review, comparing it with each other and considering the relationship between inputs and outputs. Two different times series were used to benchmark the set of twenty fitness functions. The preliminary results show the proposed method is a promising approach for efficient selecting the fitness function.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129579484","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.95
P. Rougemont, Filipe Braida do Carmo, Marden Braga Pasinato, Carlos E. Mello, Geraldo Zimbrão
This work proposes a new methodology for the Group Recommendation problem. In this approach we choose the Most Representative User (MRU) as the group medoid in a user space projection, and then generate the recommendation list based on his preferences. We evaluate our proposal by using the well-known dataset Movie lens. We have taken two different measures so as to evaluate the group recommender strategies. The obtained results seem promising and our strategy has shown an empirical robustness compared with the baselines in the literature.
{"title":"Group Recommender Systems: Exploring Underlying Information of the User Space","authors":"P. Rougemont, Filipe Braida do Carmo, Marden Braga Pasinato, Carlos E. Mello, Geraldo Zimbrão","doi":"10.1109/BRICS-CCI-CBIC.2013.95","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.95","url":null,"abstract":"This work proposes a new methodology for the Group Recommendation problem. In this approach we choose the Most Representative User (MRU) as the group medoid in a user space projection, and then generate the recommendation list based on his preferences. We evaluate our proposal by using the well-known dataset Movie lens. We have taken two different measures so as to evaluate the group recommender strategies. The obtained results seem promising and our strategy has shown an empirical robustness compared with the baselines in the literature.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130933257","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.71
Daniel S. F. Alves, D. O. Cardoso, Hugo C. C. Carneiro, F. França, P. Lima
This work investigates the effect of different data structures on the performance and accuracy of VG-RAM-based classifiers. This weightless neural model is based on RAM nodes having very large address input, what suggests the use of special data structures in order to deal with space and time computational costs. Four different data structures are explored, including the classical one used in recent VG-RAM related literature, resulting in a novel and accurate yet fast setup.
{"title":"An Empirical Study of the Influence of Data Structures on the Performance of VG-RAM Classifiers","authors":"Daniel S. F. Alves, D. O. Cardoso, Hugo C. C. Carneiro, F. França, P. Lima","doi":"10.1109/BRICS-CCI-CBIC.2013.71","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.71","url":null,"abstract":"This work investigates the effect of different data structures on the performance and accuracy of VG-RAM-based classifiers. This weightless neural model is based on RAM nodes having very large address input, what suggests the use of special data structures in order to deal with space and time computational costs. Four different data structures are explored, including the classical one used in recent VG-RAM related literature, resulting in a novel and accurate yet fast setup.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129368518","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.76
F. Corona, Zhanxing Zhu, Amauri H. Souza Junior, M. Mulas, G. Barreto, R. Baratti
In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations using Local Linear Regression (LLR). An experimental evaluation is conducted to show the performance of the SDPP and LLR and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. For the task, the results obtained on a benchmark problem consisting of a set of NIR spectra of diesel fuels and six different chemico-physical properties of those fuels are discussed. Based on the experimental results, the SDPP leads to accurate and parsimonious projections that can be effectively used in the design of estimation models based on local linear regression.
{"title":"Monitoring Diesel Fuels with Supervised Distance Preserving Projections and Local Linear Regression","authors":"F. Corona, Zhanxing Zhu, Amauri H. Souza Junior, M. Mulas, G. Barreto, R. Baratti","doi":"10.1109/BRICS-CCI-CBIC.2013.76","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.76","url":null,"abstract":"In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations using Local Linear Regression (LLR). An experimental evaluation is conducted to show the performance of the SDPP and LLR and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. For the task, the results obtained on a benchmark problem consisting of a set of NIR spectra of diesel fuels and six different chemico-physical properties of those fuels are discussed. Based on the experimental results, the SDPP leads to accurate and parsimonious projections that can be effectively used in the design of estimation models based on local linear regression.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125805621","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.17
Tiago F. B. de Sousa, Marcelo A. C. Fernandes
Optical fibers are commonly used in communications today, mainly because that the data transmission rates of those systems are faster than those in any other digital communication system. Despite this great advantage, some problems prevent the full use of optical connection: by increasing transmission rates over longer distances, the data is affected by non-linear inter-symbol interference caused by the dispersion phenomena in the fiber. Adaptive equalizers can be used to compensate for the effects caused by channel non-linear responses, restoring the originally transmitted signal. The present study discusses a proposal based on artificial neural networks, a neural equalizer. The proposal is validated through a simulated optic channel and the comparison with other adaptive equalization techniques.
{"title":"Bi-dimensional Neural Equalizer Applied to Optical Receiver","authors":"Tiago F. B. de Sousa, Marcelo A. C. Fernandes","doi":"10.1109/BRICS-CCI-CBIC.2013.17","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.17","url":null,"abstract":"Optical fibers are commonly used in communications today, mainly because that the data transmission rates of those systems are faster than those in any other digital communication system. Despite this great advantage, some problems prevent the full use of optical connection: by increasing transmission rates over longer distances, the data is affected by non-linear inter-symbol interference caused by the dispersion phenomena in the fiber. Adaptive equalizers can be used to compensate for the effects caused by channel non-linear responses, restoring the originally transmitted signal. The present study discusses a proposal based on artificial neural networks, a neural equalizer. The proposal is validated through a simulated optic channel and the comparison with other adaptive equalization techniques.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114533609","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.121
E. Christo, M. Ferreira, K. C. Alonso
The forecasting demand is the basis of strategic planning for production, sales and finances of any company. They are used for planning and control of production for planning productive system (long term) and the using (short term) of this system. With the increasing of the competition in the automobile market, there are, consequently, the increasing of concerning about establishing a balance between offering and demand of vehicles. Then come the need to calculate statistical predictions of future demands, which are translated into a real approximation of future events of the company in question. Thus, this work is divided in two stages: first - find out the best forecasting model (lower mean percentage of error between the actual and predicted) for the vehicle demand, second - analyze the residuals control charts of the best forecasting model so that to observe and draw the points that may be outside the control limits. The main goal is to plan the production of vehicle sales within a stipulated period and create scenarios for future periods.
{"title":"Use of Statistical Control for Improved Demand Forecasting","authors":"E. Christo, M. Ferreira, K. C. Alonso","doi":"10.1109/BRICS-CCI-CBIC.2013.121","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.121","url":null,"abstract":"The forecasting demand is the basis of strategic planning for production, sales and finances of any company. They are used for planning and control of production for planning productive system (long term) and the using (short term) of this system. With the increasing of the competition in the automobile market, there are, consequently, the increasing of concerning about establishing a balance between offering and demand of vehicles. Then come the need to calculate statistical predictions of future demands, which are translated into a real approximation of future events of the company in question. Thus, this work is divided in two stages: first - find out the best forecasting model (lower mean percentage of error between the actual and predicted) for the vehicle demand, second - analyze the residuals control charts of the best forecasting model so that to observe and draw the points that may be outside the control limits. The main goal is to plan the production of vehicle sales within a stipulated period and create scenarios for future periods.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122713527","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.25
Thiago M. De Almeida, Reginaldo B. Nunes, Helder R. de O. Rocha, M. Segatto, Jair A. L. Silva
The employment of genetic algorithms in parameters optimization of direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems in short-range links is reported. Experimental transmission of a 3.56 Gb/s (4-QAM subcarrier mapping) optimized DDO-OFDM system in optical back-to-back (B2B) configuration and through 20 and 40 km of uncompensated standard single-mode fiber (SSMF) was achieved.
{"title":"Performance Optimization of DDO-OFDM Systems through Genetic Algorithms","authors":"Thiago M. De Almeida, Reginaldo B. Nunes, Helder R. de O. Rocha, M. Segatto, Jair A. L. Silva","doi":"10.1109/BRICS-CCI-CBIC.2013.25","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.25","url":null,"abstract":"The employment of genetic algorithms in parameters optimization of direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems in short-range links is reported. Experimental transmission of a 3.56 Gb/s (4-QAM subcarrier mapping) optimized DDO-OFDM system in optical back-to-back (B2B) configuration and through 20 and 40 km of uncompensated standard single-mode fiber (SSMF) was achieved.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125775525","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.46
Amauri H. Souza Junior, F. Corona, Y. Miché, A. Lendasse, G. Barreto
The Minimal Learning Machine (MLM) has been recently proposed as a novel supervised learning method for regression problems aiming at reconstructing the mapping between input and output distance matrices. Estimation of the response is then achieved from the geometrical configuration of the output points. Thanks to its comprehensive formulation, the MLM is inherently capable of dealing with nonlinear problems and multidimensional output spaces. In this paper, we introduce an extension of the MLM to classification tasks, thus providing a unified framework for multiresponse regression and classification problems. On the basis of our experiments, the MLM achieves results that are comparable to many de facto standard methods for classification with the advantage of offering a computationally lighter alternative to such approaches.
{"title":"Extending the Minimal Learning Machine for Pattern Classification","authors":"Amauri H. Souza Junior, F. Corona, Y. Miché, A. Lendasse, G. Barreto","doi":"10.1109/BRICS-CCI-CBIC.2013.46","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.46","url":null,"abstract":"The Minimal Learning Machine (MLM) has been recently proposed as a novel supervised learning method for regression problems aiming at reconstructing the mapping between input and output distance matrices. Estimation of the response is then achieved from the geometrical configuration of the output points. Thanks to its comprehensive formulation, the MLM is inherently capable of dealing with nonlinear problems and multidimensional output spaces. In this paper, we introduce an extension of the MLM to classification tasks, thus providing a unified framework for multiresponse regression and classification problems. On the basis of our experiments, the MLM achieves results that are comparable to many de facto standard methods for classification with the advantage of offering a computationally lighter alternative to such approaches.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130197856","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.30
A. Engelbrecht
Particle swarm optimization (PSO) is an iterative algorithm, where particle positions and best positions are updated per iteration. The order in which particle positions and best positions are updated is referred to in this paper as an iteration strategy. Two main iteration strategies exist for PSO, namely synchronous updates and asynchronous updates. A number of studies have discussed the advantages and disadvantages of these iteration strategies. Most of these studies indicated that asynchronous updates are better than synchronous updates with respect to accuracy of the solutions obtained and the speed at which swarms converge. This study provides evidence from an extensive empirical analysis that current opinions that asynchronous updates result in faster convergence and more accurate results are not true.
{"title":"Particle Swarm Optimization: Iteration Strategies Revisited","authors":"A. Engelbrecht","doi":"10.1109/BRICS-CCI-CBIC.2013.30","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.30","url":null,"abstract":"Particle swarm optimization (PSO) is an iterative algorithm, where particle positions and best positions are updated per iteration. The order in which particle positions and best positions are updated is referred to in this paper as an iteration strategy. Two main iteration strategies exist for PSO, namely synchronous updates and asynchronous updates. A number of studies have discussed the advantages and disadvantages of these iteration strategies. Most of these studies indicated that asynchronous updates are better than synchronous updates with respect to accuracy of the solutions obtained and the speed at which swarms converge. This study provides evidence from an extensive empirical analysis that current opinions that asynchronous updates result in faster convergence and more accurate results are not true.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"61 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121298972","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}
Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.12
A. Averkin, V. Albu, S. Ulyanov, I. Povidalo
In this article a number of neural networks based on self organizing maps, that can be successfully used for dynamic object identification, is described. The structure and algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results is given.
{"title":"Dynamic Object Identification with SOM-Based Neural Networks","authors":"A. Averkin, V. Albu, S. Ulyanov, I. Povidalo","doi":"10.1109/BRICS-CCI-CBIC.2013.12","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.12","url":null,"abstract":"In this article a number of neural networks based on self organizing maps, that can be successfully used for dynamic object identification, is described. The structure and algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results is given.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124983554","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}
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