Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Exploring The Parameter Space Of A Genetic Algorithm For Training An Analog Neural Network
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Structure-Adaptable Neurocontrollers: A Hardware-Friendly Approach
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Hardware Evolution of Analog Circuits for In-situ Robotic Fault-Recovery
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Platform for Intrinsic Evolution of Analogue Neural Networks
RECONFIG '05 Proceedings of the 2005 International Conference on Reconfigurable Computing and FPGAs (ReConFig'05) on Reconfigurable Computing and FPGAs
Intrinsic Hardware Evolution of Neural Networks in Reconfigurable Analogue and Digital Devices
FCCM '06 Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Reconfigurable hardware evolution platform for a spiking neural network robotics controller
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
GRACE: generative robust analog circuit exploration
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Artificial Intelligence Review
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This paper investigates the use of a network of cascaded Field Programmable Analogue Arrays (FPAAs) to implement an evolved, analogue, Spiking Neural Network (SNN) pole balance controller. The SNN hardware platform interfaces to a simulated pole balancing model for evaluation. Performance of the evolved analogue hardware controller is compared to that of a software-based SNN controller. The evolved hardware network displays an improved tolerance to changing environments compared with networks evolved solely in simulation. The paper goes on to discuss the suitability of low density FPAA devices for analogue-centric hardware neural network platforms. It concludes by outlining some possible directions which address the observed limitations of using FPAAs for ANNs.