N. Richardson

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A novel approach to developing CAD microwave device models is presented. Traditional CAD devices are implemented using static empirical equations to describe electrical behavior. Recently, neural networks have been used in place of empirical equations to model device behavior. This paper describes the implementation of a CAD device model that utilizes a(More)
This paper describes the first implementation of an adaptable knowledge-based neural network (AKBNN) model in a high efficiency class F MMIC (monolithic microwave integrated circuit) amplifier design at Ka-band in a 0.25 /spl mu/m GaAs PHEMT technology. A single-stage amplifier based upon the AKBNN model employed shows comparable results to measured(More)
This paper describes a self-recovery algorithm for a neural network-based controller for an intelligent radiofrequency front-end amplifier. The neurocontroller provides autonomous operation, assessment and recovery capabilities. The neurocontroller is designed to reconfigure the input and output matching networks architecture, thereby providing control of(More)
The paper describes a novel approach for adjusting the matching networks of an RF front-end amplifier to maintain system performance using an adaptive control network (ACN). To maintain system performance, the matching networks of the amplifier are dynamically adjusted as a function of frequency. The ACN consists of a back-propagation neural network that(More)
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