The Experts below are selected from a list of 3264 Experts worldwide ranked by ideXlab platform

M Najim - One of the best experts on this subject based on the ideXlab platform.

  • a new feedforward neural network Hidden Layer Neuron pruning algorithm
    International Conference on Acoustics Speech and Signal Processing, 2001
    Co-Authors: Farhat Fnaiech, N Fnaiech, M Najim
    Abstract:

    This paper deals with a new approach to detect the structure (i.e. determination of the number of Hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the Hidden Layer's Neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output Layer, this pruning procedure is performed on each node in each Layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network Hidden Layer Neurons.

  • ICASSP - A new feedforward neural network Hidden Layer Neuron pruning algorithm
    2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1
    Co-Authors: Farhat Fnaiech, N Fnaiech, M Najim
    Abstract:

    This paper deals with a new approach to detect the structure (i.e. determination of the number of Hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the Hidden Layer's Neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output Layer, this pruning procedure is performed on each node in each Layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network Hidden Layer Neurons.

Bin Jiao - One of the best experts on this subject based on the ideXlab platform.

  • Extreme Learning Machine Optimized by Improved Firefly Algorithm
    DEStech Transactions on Computer Science and Engineering, 2017
    Co-Authors: Ze-kun Zhou, Bin Jiao
    Abstract:

    As a simple and effective feedforward neural network, extreme learning machine (ELM) can randomly generate the connection weight between input Layer and Hidden Layer and the Hidden Layer Neuron threshold. Extreme learning machine can be used to solve the classification problem, but its classification accuracy is not good enough. In this paper, we proposed an improved firefly algorithm called IFA and use it to select the parameters in ELM. Experimental results showed that the IFA can solve the premature problem and the classification ability of ELM can be improved by the use of IFA.

Farhat Fnaiech - One of the best experts on this subject based on the ideXlab platform.

  • a new feedforward neural network Hidden Layer Neuron pruning algorithm
    International Conference on Acoustics Speech and Signal Processing, 2001
    Co-Authors: Farhat Fnaiech, N Fnaiech, M Najim
    Abstract:

    This paper deals with a new approach to detect the structure (i.e. determination of the number of Hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the Hidden Layer's Neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output Layer, this pruning procedure is performed on each node in each Layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network Hidden Layer Neurons.

  • ICASSP - A new feedforward neural network Hidden Layer Neuron pruning algorithm
    2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1
    Co-Authors: Farhat Fnaiech, N Fnaiech, M Najim
    Abstract:

    This paper deals with a new approach to detect the structure (i.e. determination of the number of Hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the Hidden Layer's Neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output Layer, this pruning procedure is performed on each node in each Layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network Hidden Layer Neurons.

Ze-kun Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Extreme Learning Machine Optimized by Improved Firefly Algorithm
    DEStech Transactions on Computer Science and Engineering, 2017
    Co-Authors: Ze-kun Zhou, Bin Jiao
    Abstract:

    As a simple and effective feedforward neural network, extreme learning machine (ELM) can randomly generate the connection weight between input Layer and Hidden Layer and the Hidden Layer Neuron threshold. Extreme learning machine can be used to solve the classification problem, but its classification accuracy is not good enough. In this paper, we proposed an improved firefly algorithm called IFA and use it to select the parameters in ELM. Experimental results showed that the IFA can solve the premature problem and the classification ability of ELM can be improved by the use of IFA.

N Fnaiech - One of the best experts on this subject based on the ideXlab platform.

  • a new feedforward neural network Hidden Layer Neuron pruning algorithm
    International Conference on Acoustics Speech and Signal Processing, 2001
    Co-Authors: Farhat Fnaiech, N Fnaiech, M Najim
    Abstract:

    This paper deals with a new approach to detect the structure (i.e. determination of the number of Hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the Hidden Layer's Neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output Layer, this pruning procedure is performed on each node in each Layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network Hidden Layer Neurons.

  • ICASSP - A new feedforward neural network Hidden Layer Neuron pruning algorithm
    2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1
    Co-Authors: Farhat Fnaiech, N Fnaiech, M Najim
    Abstract:

    This paper deals with a new approach to detect the structure (i.e. determination of the number of Hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the Hidden Layer's Neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output Layer, this pruning procedure is performed on each node in each Layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network Hidden Layer Neurons.