Incremental Learning on a Budget and Its Application to Power Electronics
In this paper, we present an incremental learning method on a budget for embedded systems. We discuss its application for two power systems: a micro-converter for photovoltaic and a step down DC-DC-converter. This learning method is a variation of the general regression neural network but it is able to continue incremental learning on a bounded support set. The method basically learns new instances by adding new kernels. However, when the number of kernels reaches a predefined upper bound, the method selects the most effective learning option from several options: including replacing the most ineffective kernel with the new kernel, modifying of the parameters of existing kernels, and ignoring the new instance.
The proposed method is compared with other similar learning methods on a budget, which are based on kernel perceptron. Two examples of the application of the proposed method are demonstrated in power electronics. In these two examples, we show that the proposed system learns the properties of the control-objects during the services and realizes quick control.
KeywordsIncremental Learning on a budget Kernel Method micro-converter photovoltaic shadow-flicker model-based control DC-DC converter
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