The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Nikola Kasabov - One of the best experts on this subject based on the ideXlab platform.

  • 2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    Neural Networks, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

  • integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    International Joint Conference on Neural Network, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

Stefan Schliebs - One of the best experts on this subject based on the ideXlab platform.

  • 2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    Neural Networks, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

  • integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    International Joint Conference on Neural Network, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

Michael Defoinplatel - One of the best experts on this subject based on the ideXlab platform.

  • 2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    Neural Networks, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

  • integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    International Joint Conference on Neural Network, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

Susan P Worner - One of the best experts on this subject based on the ideXlab platform.

  • 2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    Neural Networks, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

  • integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
    International Joint Conference on Neural Network, 2009
    Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola Kasabov
    Abstract:

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different Representations to explore the two search spaces: A Binary Representation for optimizing feature subsets and a continuous Representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

Alberto Isidori - One of the best experts on this subject based on the ideXlab platform.

  • stabilizability by state feedback implies stabilizability by encoded state feedback
    Systems & Control Letters, 2004
    Co-Authors: Claudio De Persis, Alberto Isidori
    Abstract:

    Abstract Encoded state feedback is a term which refers to the situation in which the state feedback signal is sampled every T units of time and converted (encoded) into a Binary Representation. In this note stabilization of nonlinear systems by encoded state feedback is studied. It is shown that any nonlinear control system which can be globally asymptotically stabilized by “standard” (i.e. with no encoding) state feedback can also be globally asymptotically stabilized by encoded state feedback, provided that the number of bits used to encode the samples is not less than an explicitly determined lower bound. By means of this bound, we are able to establish a direct relationship between the size of the expected region of attraction and the data rate, under the stabilizability assumption only, a result which—to the best of our knowledge—does not have any precedent in the literature.