This paper postulates that feedforward artificial neural networks (FANN) can be used to identify parameters of electronic circuits. The problem of identification accuracy is discussed to show that ...errors can be made small by learning a multi-variable function and its partial derivatives with respect to the unknown parameters. Results of computer simulation and measurements are analysed using examples of a semiconductor diode and two bandpass filters. They show that high accuracy can be obtained with small-size, single-hidden-layer FANNs. The identification process can be made very fast, limited only by the signal propagation through the actual FANN structure, with no need for the iterative calculations that are normally required using traditional model fitting techniques.
A waveguide-cavity oscillator, applicable to power-combining circuits, has been developed using probe for coupling between active device and, cavity. No lossy stabilizing element is required. The ...control of output power, oscillation frequency, and injection locking bandwidth are performed easily. Output power of 44 mW and dc-RF conversion efficiency of 33.2 percent were obtained at 9.2GHz for a single-device low-power FET oscillator. A simple technique of cascading the pretuned oscillator modules was used to construct multiple-device oscillators incorporating up to four FET's with combining efficiency of about 100 percent.
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