With existent uses ranging from motion detection to music synthesis to financial forecasting,
recurrent neural networks have generated widespread attention. The tremendous interest in …

Recurrent neural networks have been an interesting and important part of neural network
research during the 1990's. They have already been applied to a wide variety of problems …

A recurrent neural network (RNN) is any network whose neurons send feedback signals to
each other. This concept includes a huge number of possibilities. A number of reviews …

This chapter presents an introduction to recurrent neural networks for readers familiar with
artificial neural networks in general, and multi-layer perceptrons trained with gradient …

The text discusses recurrent neural networks for prediction and offers new insights into the
learning algorithms, architectures, and stability of recurrent neural networks. It discusses …

From the Publisher: From mobile communications to robotics to space technology to medical
instrumentation, new technologies are demanding increasingly complex methods of digital …

Highlights•Recurrent neural networks (RNNs) are powerful models of neural systems.•RNNs
can be either designed or trained to perform a task.•In both cases, low dimensional …

" Analyzes the behavior, design, and implementation of artificial recurrent neural networks.
Offers methods of synthesis for associative memories. Evaluates the qualitative properties …

Recently, fully connected recurrent neural networks have been proven to be computationally
rich-at least as powerful as Turing machines. This work focuses on another network which is …

Since the outstanding and pioneering research work of Hopfield on recurrent neural
networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong …