Recurrent Neural Networks for Temporal Data Processing

Hubert Cardot

• Publisher: IntechOpen
• Year: 2011
• ISBN:
  9789535155218
• Topic: Informatica
• Subtopic: Reti neurali

The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.

Table of contents

• Recurrent Neural Networks for Temporal Data Processing
• Contents
• Preface
• Chapter 1 Double Seasonal Recurrent Neural Networks for Forecasting Short Term Electricity Load Demand in Indonesia
• Chapter 2 Advanced Methods for Time Series Prediction Using Recurrent Neural Networks
• Chapter 3 A New Application of Recurrent Neural Networks for EMG-Based Diagnosis of Carpal Tunnel Syndrome
• Chapter 4 Modeling of Hysteresis in Human Meridian System with Recurrent Neural Networks
• Chapter 5 Toward an Integrative Dynamic Recurrent Neural Network for Sensorimotor Coordination Dynamics.
• Chapter 6 Compact Internal Representation as a Functional Basis for Protocognitive Exploration of Dynamic Environments