A CMOS feedforward neural-network chip with on-chip parallel learning for oscillation cancellation

Authors:
J. Liu;M. A. Brooke;K. Hirotsu
Affiliations:
Dept. of Electr. Eng., Texas Univ., Richardson, TX;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2002

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Abstract

The paper presents a mixed signal CMOS feedforward neural-network chip with on-chip error-reduction hardware for real-time adaptation. The chip has compact on-chip weighs capable of high-speed parallel learning; the implemented learning algorithm is a genetic random search algorithm: the random weight change (RWC) algorithm. The algorithm does not require a known desired neural network output for error calculation and is suitable for direct feedback control. With hardware experiments, we demonstrate that the RWC chip, as a direct feedback controller, successfully suppresses unstable oscillations modeling combustion engine instability in real time.