The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Shyang Chang - One of the best experts on this subject based on the ideXlab platform.
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an Adaptive Learning algorithm for principal component analysis
IEEE Transactions on Neural Networks, 1995Co-Authors: Lianghwa Chen, Shyang ChangAbstract:Principal component analysis (PCA) is one of the most general purpose feature extraction methods. A variety of Learning algorithms for PCA has been proposed. Many conventional algorithms, however, will either diverge or converge very slowly if Learning rate parameters are not properly chosen. In this paper, an Adaptive Learning algorithm (ALA) for PCA is proposed. By Adaptively selecting the Learning rate parameters, we show that the m weight vectors in the ALA converge to the first m principle component vectors with almost the same rates. Comparing with the Sanger's generalized Hebbian algorithm (GHA), the ALA can quickly find the desired principal component vectors while the GHA fails to do so. Finally, simulation results are also included to illustrate the effectiveness of the ALA. >
Ralf Moller - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Learning rate control for neural gas principal component analysis
The European Symposium on Artificial Neural Networks, 2010Co-Authors: Wolfram Schenck, Ralph Welsch, Alexander Kaiser, Ralf MollerAbstract:We propose a novel algorithm for Adaptive Learning rate control for Gaussian mixture models of the NGPCA type. The core idea is to introduce a unit–specific Learning rate which is adjusted automatically depending on the match between the local principal component analysis of each unit (interpreted as Gaussian distribution) and the empirical distribution within the unit’s data partition. In contrast to fixed annealing schemes for the Learning rate, the novel algorithm is applicable to real online Learning. Two experimental studies are presented which demonstrate this important property and the general performance of this algorithm.
Qiongjie Tia - One of the best experts on this subject based on the ideXlab platform.
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self Adaptive Learning based particle swarm optimization
Information Sciences, 2011Co-Authors: Yu Wang, Thomas Weise, Jianyu Wang, O Yua, Qiongjie TiaAbstract:Particle swarm optimization (PSO) is a population-based stochastic search technique for solving optimization problems over continuous space, which has been proven to be efficient and effective in wide applications in scientific and engineering domains. However, the universality of current PSO variants, i.e., their ability to achieve good performance on a variety of different fitness landscapes, is still unsatisfying. For many practical problems, where the fitness landscapes are usually unknown, employing a trial-and-error scheme to search for the most suitable PSO variant is computationally expensive. Therefore, it is necessary to develop a more Adaptive and robust PSO version to provide users a black-box tool for various application problems. In this paper, we propose a self-Adaptive Learning based PSO (SLPSO) to make up the above demerits. SLPSO simultaneously adopts four PSO based search strategies. A probability model is used to describe the probability of a strategy being used to update a particle. The model is self-Adaptively improved according to the strategies' ability of generating better quality solutions in the past generations. In order to evaluate the performance of SLPSO, we compare it with eight state-of-the-art PSO variants on 26 numerical optimization problems with different characteristics such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise. The experimental results clearly verify the advantages of SLPSO. Moreover, a practical engineering problem, the economic load dispatch problem of power systems (ELD), is used to further evaluate SLPSO. Compared with the previous effective ELD evolutionary algorithms, SLPSO can update the best solution records.
Tunyu Chang - One of the best experts on this subject based on the ideXlab platform.
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an Adaptive Learning scheme for load balancing with zone partition in multi sink wireless sensor network
Expert Systems With Applications, 2012Co-Authors: Shengtzong Cheng, Tunyu ChangAbstract:Highlights? We propose an Adaptive Learning scheme for load balancing scheme in multi-sink WSN. ? The mobile anchor Adaptively partitions the network into several zones. ? The agent learns to balance the load in the way of reallocate the collection zones. ?The agent applies the residual energy of hotspots around sink nodes. ?The proposed QAZP scheme prolongs the lifetime of the wireless sensor network. In many researches on load balancing in multi-sink WSN, sensors usually choose the nearest sink as destination for sending data. However, in WSN, events often occur in specific area. If all sensors in this area all follow the nearest-sink strategy, sensors around nearest sink called hotspot will exhaust energy early. It means that this sink is isolated from network early and numbers of routing paths are broken. In this paper, we propose an Adaptive Learning scheme for load balancing scheme in multi-sink WSN. The agent in a centralized mobile anchor with directional antenna is introduced to Adaptively partition the network into several zones according to the residual energy of hotspots around sink nodes. In addition, machine Learning is applied to the mobile anchor to make it adaptable to any traffic pattern. Through interactions with the environment, the agent can discovery a near-optimal control policy for movement of mobile anchor. The policy can achieve minimization of residual energy's variance among sinks, which prevent the early isolation of sink and prolong the network lifetime.
Lianghwa Chen - One of the best experts on this subject based on the ideXlab platform.
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an Adaptive Learning algorithm for principal component analysis
IEEE Transactions on Neural Networks, 1995Co-Authors: Lianghwa Chen, Shyang ChangAbstract:Principal component analysis (PCA) is one of the most general purpose feature extraction methods. A variety of Learning algorithms for PCA has been proposed. Many conventional algorithms, however, will either diverge or converge very slowly if Learning rate parameters are not properly chosen. In this paper, an Adaptive Learning algorithm (ALA) for PCA is proposed. By Adaptively selecting the Learning rate parameters, we show that the m weight vectors in the ALA converge to the first m principle component vectors with almost the same rates. Comparing with the Sanger's generalized Hebbian algorithm (GHA), the ALA can quickly find the desired principal component vectors while the GHA fails to do so. Finally, simulation results are also included to illustrate the effectiveness of the ALA. >
