Analog VLSI and neural systems
Analog VLSI and neural systems
On the dynamics of small continuous-time recurrent neural networks
Adaptive Behavior - Special issue on computational neuroethology
Pulsed Neural Networks
Analogue Neural VLSI: A Pulse Stream Approach
Analogue Neural VLSI: A Pulse Stream Approach
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
IEEE Transactions on Evolutionary Computation
A family of compact genetic algorithms for intrinsic evolvable hardware
IEEE Transactions on Evolutionary Computation
Evolving analog controllers for correcting thermoacoustic instability in real hardware
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Continuous Time Recurrent Neural Networks (CTRNNs) have previously been proposed as an enabling paradigm for evolving analog electrical circuits to serve as controllers for physical devices [6]. Currently underway is the design of a CTRNN-EH VLSI chips that combines an evolutionary algorithm and a reconfigurable analog CTRNN into a single hardware device capable of learning control laws of physical devices. One potential application of this proposed device is the control and suppression of potentially damaging thermoacoustic instability in gas turbine engines. In this paper, we will present experimental evidence demonstrating the feasibility of CTRNN-EH chips for this application. We will compare our controller efficacy with that of a more traditional Linear Quadratic Regulator (LQR), showing that our evolved controllers consistently perform better and possess better generalization abilities. We will conclude with a discussion of the implications of our findings and plans for future work.