Backpropagation of errors is not only hard to justify from biological perspective but also it fails to solve problems requiring complex logic. A simpler algorithm based on generati...
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Abstract. This paper proposes a new sliding mode controller using neural networks. Multilayer neural networks with the error back-propagation learning algorithm are used to compens...
In this paper, we present the results of an experimental comparison among seven different weight initialization methods in twelve different problems. The comparison is performed by...
Processor is an important computing element in portable battery operated real time embedded system and it consumes most of the battery energy. Energy consumption, processor memory ...