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In this paper we present the fixed expansion layer (FEL) feedforward neural network designed for balancing plasticity and stability in the presence of non-stationary inputs. Catastrophic interference (or catastrophic forgetting) refers to the drastic loss of previously learned information when a neural network is trained on new or different information. The goal of the FEL network is to reduce the...
To speed up the convergence and promote the generalized performance of adaptive ridgelet neural network, we present a new model, Double-paralleled Ridgelet Neural Network, which consists of two paralleled networks — a hidden-layer adaptive ridgelet network and a single-layer feedforward neural network In order to obtain higher accuracy and learning speed, regardless of the curses of nonlinear parameters...
The field of electronic noses and gas sensing has been developing rapidly since the introduction of the silicon based sensors. There are numerous systems that can detect and indicate the level of a specific gas. We introduce here a system that is low power, small and cheap enough to be used in mobile robotic platforms while still being accurate and reliable enough for confident use. The design is...
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