: Recurrent neural networks possess interesting universal approximation capabilities, making them good candidates for time series modeling. Unfortunately, long term dependencies ar...
: This paper describes the development of neural network models for noise reduction. The networks used to enhance the performance of modeling captured signals by reducing the effec...
The ever-growing use of the Internet comes with a surging escalation of communication and data access. Most existing intrusion detection systems have assumed the one-size-fits-all...
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NA...
Abstract. We present a model of a recurrent neural network with homeostasic units, embodied in a minimalist articulated agent with a single link and joint. The configuration of th...