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2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

8-11 Sept. 2013

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Displaying Results 1 - 25 of 123
  • [Front cover]

    Publication Year: 2013, Page(s): C4
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  • [Title page i]

    Publication Year: 2013, Page(s): i
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  • [Title page iii]

    Publication Year: 2013, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2013, Page(s): iv
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  • Table of contents

    Publication Year: 2013, Page(s):v - xiii
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  • Message from the General Joint Chair

    Publication Year: 2013, Page(s): xiv
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  • Message from the General Program Chair

    Publication Year: 2013, Page(s):xv - xvi
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  • Message from the Program Chair

    Publication Year: 2013, Page(s): xvii
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  • Conference leadership and organization

    Publication Year: 2013, Page(s):xviii - xxiv
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  • Reviewers

    Publication Year: 2013, Page(s):xxv - xxvi
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  • Author's network

    Publication Year: 2013, Page(s): xxvii
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  • Dynamic Object Identification with SOM-Based Neural Networks

    Publication Year: 2013, Page(s):1 - 6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1892 KB)

    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. View full abstract»

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  • Clustering the Self-Organizing Map Based on the Neurons' Associated Pattern Sets

    Publication Year: 2013, Page(s):7 - 14
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (696 KB)

    This paper presents an automatic clustering system, built as a committee machine, which is used to cohesively partition the self-organizing map. In the proposed method, each expert from the committee machine analyzes the connections of the neuron grid based on a particular similarity matrix, and thus decides which ones should be pruned by gradually removing them and observing the intervals of stab... View full abstract»

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  • Combined Active and Semi-supervised Learning Using Particle Walking Temporal Dynamics

    Publication Year: 2013, Page(s):15 - 20
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (341 KB)

    Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to ... View full abstract»

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  • A Comparison of Two Purity-Based Algorithms When Applied to Semi-supervised Streaming Data Classification

    Publication Year: 2013, Page(s):21 - 27
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (381 KB)

    Semi-supervised learning algorithms address the problem of learning from partially labeled data. However, most of the semi-supervised classification methods proposed in the literature considers a stationary distribution of data. Which means that future data patterns tend to conform to the data distribution presented in data set throughout the application lifetime. However, for plenty of new variet... View full abstract»

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  • Neural Assemblies and Finite State Automata

    Publication Year: 2013, Page(s):28 - 33
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (470 KB)

    Neural assembly computing (NAC) is a framework for investigating computational operations realized by spiking cell assemblies and for designing spiking neural machines. NAC concerns the way assemblies interact and how it results in information processing with causal and hierarchical relations. In addition, NAC investigates how assemblies represent states of the world, how they control data flux ca... View full abstract»

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  • Bi-dimensional Neural Equalizer Applied to Optical Receiver

    Publication Year: 2013, Page(s):34 - 39
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (393 KB)

    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 ... View full abstract»

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  • Specialized MLP Classifiers to Support the Isolation of Patients Suspected of Pulmonary Tuberculosis

    Publication Year: 2013, Page(s):40 - 45
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (345 KB)

    Tuberculosis is an infectious disease widely present in developing countries, which is largely motivated by the difficulty of a rapid and efficient diagnosis. In order to reduce the number of patients suspected of having TB unnecessarily isolated in hospitals, thus optimize the use of health resources, we propose a systematic procedure for developing a decision support system based on specialized ... View full abstract»

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  • Structural Relationships between Spiking Neural Networks and Functional Samples

    Publication Year: 2013, Page(s):46 - 54
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (357 KB)

    Models of spiking neural networks have a great potential to become a crucial tool in the development of complex network theory. Of particular interest, these models can be used to better understand the important class of brain functional networks, which are frequently studied in the context of computational network analysis. A fundamental question is whether functional connectivity sampling via su... View full abstract»

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  • New Genetic Operators for the Evolutionary Algorithm for Clustering

    Publication Year: 2013, Page(s):55 - 59
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (353 KB)

    Finding a good clustering solution for an unknown problem is a challenging task. Evolutionary algorithms have proved to be reliable methods to search for high quality solutions to complex problems. The present paper proposes a new set of genetic operators for the Fast Evolutionary Algorithm for Clustering (Fast-EAC) to improve the solution quality and computational efficiency. The new algorithm, c... View full abstract»

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  • A New Algorithm Based on Differential Evolution for Combinatorial Optimization

    Publication Year: 2013, Page(s):60 - 66
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB)

    Differential evolution (DE) was originally designed to solve continuous optimization problems, but recent works have been investigating this algorithm for tackling combinatorial optimization (CO), particularly in permutation-based combinatorial problems. However, most DE approaches for combinatorial optimization are not general approaches to CO, being exclusive for per mutational problems and ofte... View full abstract»

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  • Evolved Linker Gene Expression Programming: A New Technique for Symbolic Regression

    Publication Year: 2013, Page(s):67 - 74
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (383 KB)

    This paper utilises Evolved Linker Gene Expression Programming (EL-GEP), a new variant of Gene Expression Programming (GEP), to solve symbolic regression and sequence induction problems. The new technique was first proposed in [6] to evolve modularity in robotic behaviours. The technique extends the GEP algorithm by incorporating a new alphabetic set (linking set) from which genome linking functio... View full abstract»

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  • Estimation of Distribution Algorithm Based on a Multivariate Extension of the Archimedean Copula

    Publication Year: 2013, Page(s):75 - 80
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (381 KB)

    This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and eliti... View full abstract»

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  • An Evolutionary Approach to Hard Test Case Generation for Shortest Common Superstring Problem

    Publication Year: 2013, Page(s):81 - 85
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (325 KB)

    The shortest common superstring problem has important applications in computational biology (e.g. genome assembly) and data compression. This problem is NP-hard, but several heuristic algorithms proved to be efficient for this problem. For example, for the algorithm known as GREEDY it was shown that, if the optimal superstring has the length of N, it produces an answer with length not exceeding 3.... View full abstract»

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  • Performance Optimization of DDO-OFDM Systems through Genetic Algorithms

    Publication Year: 2013, Page(s):86 - 91
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (979 KB)

    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... View full abstract»

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