A neuromorphic controller with a human teacher
- 1 January 1988
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 52, 595-602 vol.2
- https://doi.org/10.1109/icnn.1988.23976
Abstract
Trainable adaptive controllers (TACs) are a subset of process controllers in which much of the design is done online by means of training rather than programming. The authors show how a neural-network-based architecture may be used to implement a general-purpose TAC. An example of controlling a cart-pole system (an inverted pendulum mounted on a cart) is provided. It is found that filtering of the human-teacher training data, using a dynamic model of the teacher, significantly improves the neuromorphic TAC's performance.Keywords
This publication has 15 references indexed in Scilit:
- A massively parallel architecture for a self-organizing neural pattern recognition machinePublished by Elsevier ,2005
- A multilayered neural network controllerIEEE Control Systems Magazine, 1988
- Neural network architecture for controlIEEE Control Systems Magazine, 1988
- A hierarchical neural-network model for control and learning of voluntary movementBiological Cybernetics, 1987
- Absolute Stability of Global Pattern Formation and Parallel Memory Storage by Competitive Neural NetworksPublished by Elsevier ,1987
- Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuitIEEE Transactions on Circuits and Systems, 1986
- “Neural” computation of decisions in optimization problemsBiological Cybernetics, 1985
- Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, 1984
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- Self-organized formation of topologically correct feature mapsBiological Cybernetics, 1982