Search Results - Ludermir, T. B.

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  1. 1
    Many Objective Particle Swarm Optimization

    Many Objective Particle Swarm Optimization by Figueiredo, E.M.N., Ludermir, T.B., Bastos-Filho, C.J.A.

    Published in Information sciences
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  2. 2
    Hybrid Training Method for MLP: Optimization of Architecture and Training

    Hybrid Training Method for MLP: Optimization of Architecture and Training by Zanchettin, C., Ludermir, T. B., Almeida, L. M.

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  3. 3
    Wavelet filter for noise reduction and signal compression in an artificial nose

    Wavelet filter for noise reduction and signal compression in an artificial nose by Zanchettin, C., Ludermir, T.B.

    Published in Applied soft computing
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  4. 4
    Polypyrrole based aroma sensor

    Polypyrrole based aroma sensor by de Souza, J.E.G., Neto, B.B., dos Santos, F.L., de Melo, C.P., Santos, M.S., Ludermir, T.B.

    Published in Synthetic metals
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  5. 5
    An Optimization Methodology for Neural Network Weights and Architectures

    An Optimization Methodology for Neural Network Weights and Architectures by Ludermir, T.B., Yamazaki, A., Zanchettin, C.

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  6. 6
    Optimization of the weights and asymmetric activation function family of neural network for time series forecasting

    Optimization of the weights and asymmetric activation function family of neural network for time series forecasting by Gomes, Gecynalda S. da S., Ludermir, Teresa B.

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  7. 7
    Equivalence between RAM-based neural networks and probabilistic automata

    Equivalence between RAM-based neural networks and probabilistic automata by de Souto, M.C.P., Ludermir, T.B., de Oliveira, W.R.

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  8. 8
    HYBRID NEURAL SYSTEMS FOR PATTERN RECOGNITION IN ARTIFICIAL NOSES

    HYBRID NEURAL SYSTEMS FOR PATTERN RECOGNITION IN ARTIFICIAL NOSES by ZANCHETTIN, CLEBER, LUDERMIR, TERESA B.

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  9. 9
    Meta-learning approaches to selecting time series models

    Meta-learning approaches to selecting time series models by Prudêncio, Ricardo B.C., Ludermir, Teresa B.

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  10. 10
    Cooperative Group Search Optimization

    Cooperative Group Search Optimization by Pacifico, L. D. S., Ludermir, T. B.

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  11. 11
    Effect of the PSO Topologies on the Performance of the PSO-ELM

    Effect of the PSO Topologies on the Performance of the PSO-ELM by Figueiredo, E. M. N., Ludermir, T. B.

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  12. 12
    An evolutionary extreme learning machine based on group search optimization

    An evolutionary extreme learning machine based on group search optimization by Silva, D. N. G., Pacifico, L. D. S., Ludermir, T. B.

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  13. 13
    Automatic text categorization: case study

    Automatic text categorization: case study by Correa, R.F., Ludermir, T.B.

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  14. 14
    Improved group search optimization based on opposite populations for feedforward networks training with weight decay

    Improved group search optimization based on opposite populations for feedforward networks training with weight decay by Pacifico, L. D. S., Ludermir, T. B.

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  15. 15
    Ranking and selecting clustering algorithms using a meta-learning approach

    Ranking and selecting clustering algorithms using a meta-learning approach by de Souto, M.C.P., Prudencio, R.B.C., Soares, R.G.F., de Araujo, D.S.A., Costa, I.G., Ludermir, T.B., Schliep, A.

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  16. 16
    Forecasting the IBOVESPA using NARX networks and random walk model

    Forecasting the IBOVESPA using NARX networks and random walk model by Oliveira, E.M.J., Ludermir, T.B.

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  17. 17
    Modulatory interaction as a support to modeling neural substrates of the decision process

    Modulatory interaction as a support to modeling neural substrates of the decision process by Filho, J.D., Ludermir, T.B.

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  18. 18
    Global optimization methods for designing and training neural networks

    Global optimization methods for designing and training neural networks by Yamazaki, A., Ludermir, T.B., de Souto, M.C.P.

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  19. 19
    Neural networks for the analysis of common mental disorders factors

    Neural networks for the analysis of common mental disorders factors by Lopes, C.R.S., Ludermir, T.B., de Souto, M.C.P., Ludermir, A.B.

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  20. 20
    Evolving Neural Networks Using Differential Evolution with Neighborhood-Based Mutation and Simple Subpopulation Scheme

    Evolving Neural Networks Using Differential Evolution with Neighborhood-Based Mutation and Simple Subpopulation Scheme by Mineu, N. L., da Silva, A. J., Ludermir, T. B.

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