The Experts below are selected from a list of 6861 Experts worldwide ranked by ideXlab platform

David B Rosen - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

Gail A Carpenter - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

Stephen Grossberg - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

John H Reynolds - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

Natalya Markuzon - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >

  • fuzzy artmap a neural network architecture for incremental supervised learning of analog multidimensional maps
    IEEE Transactions on Neural Networks, 1992
    Co-Authors: Gail A Carpenter, Stephen Grossberg, Natalya Markuzon, John H Reynolds, David B Rosen
    Abstract:

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a Piecewise-Continuous Function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >