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

Gisbert Schneider - One of the best experts on this subject based on the ideXlab platform.

  • optimized particle swarm optimization opso and its application to artificial Neural Network Training
    BMC Bioinformatics, 2006
    Co-Authors: Michael Meissner, Michael Schmuker, Gisbert Schneider
    Abstract:

    Background Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to Neural Network Training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.

  • Optimized Particle Swarm Optimization (OPSO) and its application to artificial Neural Network Training
    BMC Bioinformatics, 2006
    Co-Authors: Michael Meissner, Michael Schmuker, Gisbert Schneider
    Abstract:

    BACKGROUND: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to Neural Network Training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. RESULTS: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to Neural Network Training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, Training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with Training-, test- and validation data was obtained. CONCLUSION: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.

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

  • optimized particle swarm optimization opso and its application to artificial Neural Network Training
    BMC Bioinformatics, 2006
    Co-Authors: Michael Meissner, Michael Schmuker, Gisbert Schneider
    Abstract:

    Background Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to Neural Network Training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.

  • Optimized Particle Swarm Optimization (OPSO) and its application to artificial Neural Network Training
    BMC Bioinformatics, 2006
    Co-Authors: Michael Meissner, Michael Schmuker, Gisbert Schneider
    Abstract:

    BACKGROUND: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to Neural Network Training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. RESULTS: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to Neural Network Training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, Training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with Training-, test- and validation data was obtained. CONCLUSION: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.

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

  • optimized particle swarm optimization opso and its application to artificial Neural Network Training
    BMC Bioinformatics, 2006
    Co-Authors: Michael Meissner, Michael Schmuker, Gisbert Schneider
    Abstract:

    Background Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to Neural Network Training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.

  • Optimized Particle Swarm Optimization (OPSO) and its application to artificial Neural Network Training
    BMC Bioinformatics, 2006
    Co-Authors: Michael Meissner, Michael Schmuker, Gisbert Schneider
    Abstract:

    BACKGROUND: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to Neural Network Training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. RESULTS: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to Neural Network Training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, Training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with Training-, test- and validation data was obtained. CONCLUSION: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.

Moumen T. El-melegy - One of the best experts on this subject based on the ideXlab platform.

  • Random Sampler M-Estimator Algorithm With Sequential Probability Ratio Test for Robust Function Approximation Via Feed-Forward Neural Networks
    IEEE Transactions on Neural Networks and Learning Systems, 2013
    Co-Authors: Moumen T. El-melegy
    Abstract:

    This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward Neural Network. Although it is of high importance in practical applications, this problem has not received careful attention from the Neural Network research community. One recent approach to solving this problem is to use a Neural Network Training algorithm based on the random sample consensus (RANSAC) framework. This paper proposes a new algorithm that offers two enhancements over the original RANSAC algorithm. The first one improves the algorithm accuracy and robustness by employing an M-estimator cost function to decide on the best estimated model from the randomly selected samples. The other one improves the time performance of the algorithm by utilizing a statistical pretest based on Wald's sequential probability ratio test. The proposed algorithm is successfully evaluated on synthetic and real data, contaminated with varying degrees of outliers, and compared with existing Neural Network Training algorithms.

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

  • a review of heuristic global optimization based artificial Neural Network Training approahes
    IAES International Journal of Artificial Intelligence, 2017
    Co-Authors: Geraldine Bessie D Amali, M Dinakaran
    Abstract:

    Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a Neural Network involves minimizing the mean square error between the target and Network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to Neural Network Training is of interest. This paper reviews the various heuristic global optimization algorithms used for Training feedforward Neural Networks and recurrent Neural Networks. The Training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the Neural Network. The paper concludes by suggesting directions for novel ANN Training algorithms based on recent advances in global optimization.