The Experts below are selected from a list of 247251 Experts worldwide ranked by ideXlab platform

Bernd Fritzke - One of the best experts on this subject based on the ideXlab platform.

  • ICANN - A Self-Organizing Network that Can Follow Non-stationary Distributions
    Lecture Notes in Computer Science, 1997
    Co-Authors: Bernd Fritzke
    Abstract:

    A new on-line criterion for identifying “useless” neurons of a Self-Organizing Network is proposed. When this criterion is used in the context of the (formerly developed) growing neural gas model to guide deletions of units, the resulting method is able to closely track nonstationary distributions. Slow changes of the distribution are handled by adaptation of existing units. Rapid changes are handled by removal of “useless” neurons and subsequent insertions of new units in other places.

  • growing cell structures a self organizing Network for unsupervised and supervised learning
    Neural Networks, 1994
    Co-Authors: Bernd Fritzke
    Abstract:

    Abstract We present a new Self-Organizing neural Network model that has two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches (e.g., the Kohonen feature map) is the ability of the model to automatically find a suitable Network structure and size. This is achieved through a controlled growth process that also includes occasional removal of units. The second variant of the model is a supervised learning method that results from the combination of the above-mentioned Self-Organizing Network with the radial basis function (RBF) approach. In this model it is possible—in contrast to earlier approaches—to perform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small Networks that generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented that are better than any results previously published.

  • Growing Cell Structures – a Self-Organizing Network in k Dimensions
    Artificial Neural Networks, 1992
    Co-Authors: Bernd Fritzke
    Abstract:

    In this paper an improved version of a Self-Organizing Network model is described which has been proposed at the ICANN-91[3] and since then has been applied to various problems [1,2,5]. The improvements presented here are the generalization of the model to arbitrary dimension and the introduction of a local estimate of the probability density. The latter leads to a very clear distinction between necessary and superfluous neurons with respect to modeling a given probability distribution. This makes it possible to automatically generate Network structures that are nearly optimally suited for the distribution at hand.

Eitan Altman - One of the best experts on this subject based on the ideXlab platform.

  • Self-Optimizing load balancing with backhaul-constrained radio access Networks
    IEEE Wireless Communications Letters, 2015
    Co-Authors: Abdoulaye Tall, Zwi Altman, Eitan Altman
    Abstract:

    Self-Organizing Network (SON) technology aims at autonomously deploying, optimizing and repairing the Radio Access Networks (RAN). SON algorithms typically use Key Performance Indicators (KPIs) from the RAN. It is shown that in certain cases, it is essential to take into account the impact of the backhaul state in the design of the SON algorithm. We revisit the Base Station (BS) load definition taking into account the backhaul state. We provide an analytical formula for the load along with a simple estimator for both elastic and guaranteed bit-rate (GBR) traffic. We incorporate the proposed load estimator in a self-optimized load balancing algorithm. Simulation results for a backhaul constrained heterogeneous Network illustrate how the correct load definition can guarantee a proper operation of the SON algorithm.

Hamed Yaghoubi Shahir - One of the best experts on this subject based on the ideXlab platform.

  • a collaborative decision support model for marine safety and security operations
    DIPES BICC, 2010
    Co-Authors: Uwe Glässer, Hans Wehn, Piper Jackson, Ali Khalili Araghi, Hamed Yaghoubi Shahir
    Abstract:

    Collaboration and self-organization are hallmarks of many biological systems. We present the design for an intelligent decision support system that employs these characteristics: it works through a collaborative, Self-Organizing Network of intelligent agents. Developed for the realm of Marine Safety and Security, the goal of the system is to assist in the management of a complex array of resources in both a routine and emergency role. Notably, this system must be able to handle a dynamic environment and the existence of uncertainty. The decentralized control structure of a collaborative Self-Organizing system reinforces its adaptiveness, robustness and scalability in critical situations.

  • DIPES/BICC - A Collaborative Decision Support Model for Marine Safety and Security Operations
    Distributed Parallel and Biologically Inspired Systems, 2010
    Co-Authors: Uwe Glässer, Hans Wehn, Piper Jackson, Ali Khalili Araghi, Hamed Yaghoubi Shahir
    Abstract:

    Collaboration and self-organization are hallmarks of many biological systems. We present the design for an intelligent decision support system that employs these characteristics: it works through a collaborative, Self-Organizing Network of intelligent agents. Developed for the realm of Marine Safety and Security, the goal of the system is to assist in the management of a complex array of resources in both a routine and emergency role. Notably, this system must be able to handle a dynamic environment and the existence of uncertainty. The decentralized control structure of a collaborative Self-Organizing system reinforces its adaptiveness, robustness and scalability in critical situations.

Anssi Tauriainen - One of the best experts on this subject based on the ideXlab platform.

  • can you hear me now a call detail record based end to end diagnostics system for mobile Networks
    Conference on Network and Service Management, 2019
    Co-Authors: Anssi Tauriainen
    Abstract:

    Automation of mobile Network fault diagnostics and troubleshooting is critical for successful transformation to new Network technologies such as 5G and core Network Function Virtualization (NFV). This paper presents a decision tree-based call detail record (CDR) labeling process, which is used to construct an automated end-to-end diagnostics system for mobile Network faults. The presented diagnostics system will enable the utilization of automated troubleshooting systems, and the execution of automated corrective actions in third party systems such as Self-Organizing Network (SON) and NFV domain orchestrator.

  • CNSM - Can you hear me now? A call detail record based end-to-end diagnostics system for mobile Networks
    2019 15th International Conference on Network and Service Management (CNSM), 2019
    Co-Authors: Anssi Tauriainen
    Abstract:

    Automation of mobile Network fault diagnostics and troubleshooting is critical for successful transformation to new Network technologies such as 5G and core Network Function Virtualization (NFV). This paper presents a decision tree-based call detail record (CDR) labeling process, which is used to construct an automated end-to-end diagnostics system for mobile Network faults. The presented diagnostics system will enable the utilization of automated troubleshooting systems, and the execution of automated corrective actions in third party systems such as Self-Organizing Network (SON) and NFV domain orchestrator.

Abdoulaye Tall - One of the best experts on this subject based on the ideXlab platform.

  • Self-Optimizing load balancing with backhaul-constrained radio access Networks
    IEEE Wireless Communications Letters, 2015
    Co-Authors: Abdoulaye Tall, Zwi Altman, Eitan Altman
    Abstract:

    Self-Organizing Network (SON) technology aims at autonomously deploying, optimizing and repairing the Radio Access Networks (RAN). SON algorithms typically use Key Performance Indicators (KPIs) from the RAN. It is shown that in certain cases, it is essential to take into account the impact of the backhaul state in the design of the SON algorithm. We revisit the Base Station (BS) load definition taking into account the backhaul state. We provide an analytical formula for the load along with a simple estimator for both elastic and guaranteed bit-rate (GBR) traffic. We incorporate the proposed load estimator in a self-optimized load balancing algorithm. Simulation results for a backhaul constrained heterogeneous Network illustrate how the correct load definition can guarantee a proper operation of the SON algorithm.