scholarly journals A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects

10.5772/63387 ◽  
2016 ◽  
Author(s):  
Mohamad Mostafa ◽  
Giuseppe Oliveri ◽  
Werner G. Teich ◽  
Jürgen Lindner
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Continuous Time ◽  
Hardware Implementation ◽  
Implementation Aspects

scholarly journals Active Shape Discrimination with Compliant Bodies as Reservoir Computers

2016 ◽  
Vol 22 (2) ◽  
pp. 241-268 ◽  
Author(s):  
Chris Johnson ◽  
Andrew Philippides ◽  
Philip Husbands
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Continuous Time ◽  
Complex Dynamics ◽  
The Body ◽  
Control Problems ◽  
Reservoir Computing ◽  
Shape Discrimination ◽  
Reflexive Behavior ◽  
Processing Information

Compliant bodies with complex dynamics can be used both to simplify control problems and to lead to adaptive reflexive behavior when engaged with the environment in the sensorimotor loop. By revisiting an experiment introduced by Beer and replacing the continuous-time recurrent neural network therein with reservoir computing networks abstracted from compliant bodies, we demonstrate that adaptive behavior can be produced by an agent in which the body is the main computational locus. We show that bodies with complex dynamics are capable of integrating, storing, and processing information in meaningful and useful ways, and furthermore that with the addition of the simplest of nervous systems such bodies can generate behavior that could equally be described as reflexive or minimally cognitive.

A STUDY OF TRACK FINDING WITH A NEURAL NETWORK ALGORITHM

1992 ◽  
Vol 03 (supp01) ◽  
pp. 303-308
Author(s):  
Giuseppe Barbagli ◽  
Guido Castellini ◽  
Gregorio Landi ◽  
Stefano Vettori
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Hardware Implementation ◽  
Initial Conditions ◽  
Hopfield Model ◽  
Network Algorithm ◽  
Random Initial Conditions ◽  
Neural Network Algorithm

We have investigated the problem of track finding with a recurrent neural network algorithm based on the Hopfield model and considered the possibility of a hardware implementation with DSP’s. Starting from a set of signal points we define track segments and set a cut on the length to keep the size of the network reasonable. Those segments surviving the cut are associated to neurons. A geometric coupling of neighbouring segments is used to select smooth combinations of them. Given random initial conditions the network converges to a solution. The method may be applied to a variety of curves.

Application of chaotic dynamics in a recurrent neural network to control: hardware implementation into a novel autonomous roving robot

2008 ◽  
Vol 99 (3) ◽  
pp. 185-196 ◽  
Author(s):  
Yongtao Li ◽  
Shuhei Kurata ◽  
Shogo Morita ◽  
So Shimizu ◽  
Daigo Munetaka ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Chaotic Dynamics ◽  
Hardware Implementation
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