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Reinforcement learning (RL) is a popular learning paradigm to adaptive learning control of nonlinear systems, and is able to work without an explicit model. However, learning from scratch, i.e., without any a priori knowledge, is a daunting undertaking, which results in long training time and instability of learning process with large continuous state space. For physical systems, one must consider...
When performing classification of large set of samples, neural trees (NTs) are preferably used. To circumvent the problem of poor generalization of neural trees, hybrid neural trees have been proposed. Recently hybrid SVM based neural tree has been shown to be an effective binary classifier. In this paper, we examine the performance of SVM based neural trees relative to the nonlinear SVMs. We observe...
In this paper, we investigate the potential of support vector machines (SVMs) for power quality data mining in electrical power systems. Modified wavelet transform, known as S-transform, has been used to extract unique features of the various power quality disturbances. Feature vectors from S-transform analysis are used to train the SVM classifier. Various multi-class SVM algorithms have been applied...
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