The Experts below are selected from a list of 97845 Experts worldwide ranked by ideXlab platform

J Ctyroky - One of the best experts on this subject based on the ideXlab platform.

  • improved bidirectional mode expansion Propagation Algorithm based on fourier series
    Journal of Lightwave Technology, 2007
    Co-Authors: J Ctyroky
    Abstract:

    In this paper, an improved version of a 2-D bidirectional eigenmode expansion Propagation Algorithm based on Fourier series expansion for modeling optical field distribution in waveguide devices is presented. The Algorithm is very simple, numerically robust, and inherently reciprocal. It does not require root searching in the complex plane. Proper truncation rules are used to ensure good convergence properties for TM-polarized waves. Perfectly matched layers as absorbing boundary conditions can be implemented in a very simple way using complex coordinate stretching. The approach represents a transition between purely modal and Fourier expansion methods for modeling guided-wave photonic structures.

Markku Juntti - One of the best experts on this subject based on the ideXlab platform.

  • positioning for nlos Propagation Algorithm derivations and cramer rao bounds
    IEEE Transactions on Vehicular Technology, 2007
    Co-Authors: Honglei Miao, Markku Juntti
    Abstract:

    Mobile positioning has drawn significant attention in recent years. Nonline-of-sight (NLOS) Propagation error is the dominant error source in mobile positioning. Most previous research in this area has focused on NLOS identification and mitigation. In this paper, we investigate new positioning Algorithms to take advantage of the NLOS Propagation paths rather than canceling them. Based on a prior information about the NLOS path, a geometrical approach is proposed to estimate mobile location by using two NLOS paths. On top of this, the least-squares (LS)-based position estimation Algorithm is developed to take multiple NLOS paths into account, and its performance in terms of root mean-square error (RMSE) is analyzed. A general LS Algorithm considering both LOS and NLOS paths is also derived, and the maximum likelihood-based Algorithm is presented to jointly estimate the mobile's and scatterers' positions. The Cramer-Rao lower bound on the RMSE is derived for the benchmark of the performance comparison. The performance of the proposed Algorithms is evaluated analytically and is done via computer simulations. Numerical results demonstrate that the derived analytical results closely match the simulated results.

  • positioning for nlos Propagation Algorithm derivations and cramer rao bounds
    International Conference on Acoustics Speech and Signal Processing, 2006
    Co-Authors: Honglei Miao, Markku Juntti
    Abstract:

    Mobile positioning has drawn significant attention in recent years. In dealing with the non-line-of-sight (NLOS) Propagation error, the dominant error source in the mobile positioning, most previous research in this area has focused on the NLOS identification and mitigation. In this paper, we investigate new positioning Algorithms to take advantage of the NLOS Propagation paths rather than cancelling them. Based on the prior information about the NLOS path, a least squares based position estimation Algorithm is developed and its performance in terms of root mean square error (RMSE) is also analyzed. Furthermore, the maximum likelihood based Algorithm is presented to jointly estimate the mobile's and scatterers' positions. The Cramer-Rao lower bound on the RMSE is derived for the benchmark of the performance comparison. Finally, the performances of the proposed Algorithms are evaluated analytically and via computer simulations. Numerical results demonstrate that the simulated results closely match the derived analytical results.

Jemal H Abawajy - One of the best experts on this subject based on the ideXlab platform.

  • an accelerated particle swarm optimization based levenberg marquardt back Propagation Algorithm
    International Conference on Neural Information Processing, 2014
    Co-Authors: Nazri Mohd Nawi, Abdullah Khan, M Z Rehman, Maslina Abdul Aziz, Tutut Herawan, Jemal H Abawajy
    Abstract:

    The Levenberg Marquardt (LM) Algorithm is one of the most effective Algorithms in speeding up the convergence rate of the Artificial Neural Networks (ANN) with Multilayer Perceptron (MLP) architectures. However, the LM Algorithm suffers the problem of local minimum entrapment. Therefore, we introduce several improvements to the Levenberg Marquardt Algorithm by training the ANNs with meta-heuristic nature inspired Algorithm. This paper proposes a hybrid technique Accelerated Particle Swarm Optimization using Levenberg Marquardt (APSO_LM) to achieve faster convergence rate and to avoid local minima problem. These techniques are chosen since they provide faster training for solving pattern recognition problems using the numerical optimization technique.The performances of the proposed Algorithm is evaluated using some bench mark of classification’s datasets. The results are compared with Artificial Bee Colony (ABC) Algorithm using Back Propagation Neural Network (BPNN) Algorithm and other hybrid variants.Based on the experimental result, the proposed Algorithms APSO_LM successfully demonstrated better performance as compared to other existing Algorithms in terms of convergence speed and Mean Squared Error (MSE) by introducing the error and accuracy in network convergence.

Honglei Miao - One of the best experts on this subject based on the ideXlab platform.

  • positioning for nlos Propagation Algorithm derivations and cramer rao bounds
    IEEE Transactions on Vehicular Technology, 2007
    Co-Authors: Honglei Miao, Markku Juntti
    Abstract:

    Mobile positioning has drawn significant attention in recent years. Nonline-of-sight (NLOS) Propagation error is the dominant error source in mobile positioning. Most previous research in this area has focused on NLOS identification and mitigation. In this paper, we investigate new positioning Algorithms to take advantage of the NLOS Propagation paths rather than canceling them. Based on a prior information about the NLOS path, a geometrical approach is proposed to estimate mobile location by using two NLOS paths. On top of this, the least-squares (LS)-based position estimation Algorithm is developed to take multiple NLOS paths into account, and its performance in terms of root mean-square error (RMSE) is analyzed. A general LS Algorithm considering both LOS and NLOS paths is also derived, and the maximum likelihood-based Algorithm is presented to jointly estimate the mobile's and scatterers' positions. The Cramer-Rao lower bound on the RMSE is derived for the benchmark of the performance comparison. The performance of the proposed Algorithms is evaluated analytically and is done via computer simulations. Numerical results demonstrate that the derived analytical results closely match the simulated results.

  • positioning for nlos Propagation Algorithm derivations and cramer rao bounds
    International Conference on Acoustics Speech and Signal Processing, 2006
    Co-Authors: Honglei Miao, Markku Juntti
    Abstract:

    Mobile positioning has drawn significant attention in recent years. In dealing with the non-line-of-sight (NLOS) Propagation error, the dominant error source in the mobile positioning, most previous research in this area has focused on the NLOS identification and mitigation. In this paper, we investigate new positioning Algorithms to take advantage of the NLOS Propagation paths rather than cancelling them. Based on the prior information about the NLOS path, a least squares based position estimation Algorithm is developed and its performance in terms of root mean square error (RMSE) is also analyzed. Furthermore, the maximum likelihood based Algorithm is presented to jointly estimate the mobile's and scatterers' positions. The Cramer-Rao lower bound on the RMSE is derived for the benchmark of the performance comparison. Finally, the performances of the proposed Algorithms are evaluated analytically and via computer simulations. Numerical results demonstrate that the simulated results closely match the derived analytical results.

Nazri Mohd Nawi - One of the best experts on this subject based on the ideXlab platform.

  • an accelerated particle swarm optimization based levenberg marquardt back Propagation Algorithm
    International Conference on Neural Information Processing, 2014
    Co-Authors: Nazri Mohd Nawi, Abdullah Khan, M Z Rehman, Maslina Abdul Aziz, Tutut Herawan, Jemal H Abawajy
    Abstract:

    The Levenberg Marquardt (LM) Algorithm is one of the most effective Algorithms in speeding up the convergence rate of the Artificial Neural Networks (ANN) with Multilayer Perceptron (MLP) architectures. However, the LM Algorithm suffers the problem of local minimum entrapment. Therefore, we introduce several improvements to the Levenberg Marquardt Algorithm by training the ANNs with meta-heuristic nature inspired Algorithm. This paper proposes a hybrid technique Accelerated Particle Swarm Optimization using Levenberg Marquardt (APSO_LM) to achieve faster convergence rate and to avoid local minima problem. These techniques are chosen since they provide faster training for solving pattern recognition problems using the numerical optimization technique.The performances of the proposed Algorithm is evaluated using some bench mark of classification’s datasets. The results are compared with Artificial Bee Colony (ABC) Algorithm using Back Propagation Neural Network (BPNN) Algorithm and other hybrid variants.Based on the experimental result, the proposed Algorithms APSO_LM successfully demonstrated better performance as compared to other existing Algorithms in terms of convergence speed and Mean Squared Error (MSE) by introducing the error and accuracy in network convergence.

  • a new levenberg marquardt based back Propagation Algorithm trained with cuckoo search
    Procedia Technology, 2013
    Co-Authors: Nazri Mohd Nawi, Abdullah Khan, M Z Rehman
    Abstract:

    Back Propagation training Algorithm is widely used techniques in artificial neural network and is also very popular optimization task in finding an optimal weight sets during the training process. However, traditional back Propagation Algorithms have some drawbacks such as getting stuck in local minimum and slow speed of convergence. This research proposed an improved Levenberg Marquardt (LM) based back Propagation (BP) trained with Cuckoo search Algorithm for fast and improved convergence speed of the hybrid neural networks learning method. The performance of the proposed Algorithm is compared with Artificial Bee Colony (ABC) and the other hybridized procedure of its kind. The simulation outcomes show that the proposed Algorithm performed better than other Algorithm used in this study in term of convergence speed and rate.

  • an improved learning Algorithm based on the broyden fletcher goldfarb shanno bfgs method for back Propagation neural networks
    Intelligent Systems Design and Applications, 2006
    Co-Authors: Nazri Mohd Nawi, Meghana R Ransing, R S Ransing
    Abstract:

    The Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization Algorithm usually used for nonlinear least squares is presented and is combined with the modified back Propagation Algorithm yielding a new fast training multilayer perceptron (MLP) Algorithm (BFGS/AG). The approaches presented in the paper consist of three steps: (1) Modification on standard back Propagation Algorithm by introducing "gain variation" term of the activation function, (2) Calculating the gradient descent on error with respect to the weights and gains values and (3) the determination of the new search direction by exploiting the information calculated by gradient descent in step (2) as well as the previous search direction. The new approach improved the training efficiency of back Propagation Algorithm by adaptively modifying the initial search direction. Performance of the proposed method is demonstrated by comparing to the Broyden-Fletcher-Goldfarb-Shanno Algorithm from neural network toolbox for the chosen benchmark. The results show that the number of iterations required by this Algorithm to converge is less than 15% of what is required by the standard BFGS and neural network toolbox Algorithm. It considerably improves the convergence rate significantly faster because of it new efficient search direction.