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An effective approach, based on neural networks, to the fault diagnosis of dc-ac resonant inverters is presented. A MultiLayer MultiValued Neuron neural Network (MLMVNN) with a complex QR-decomposition is used to identify parameter value changing (i.e. fault detection) on a Class-E resonant inverter through steady state measurements of voltages and currents.
This paper presents an effective approach to the fault diagnosis of PWM DC-DC power converters. It is based on a MultiLayer Multi-Valued Neuron Neural Network (MLMVNN) with a complex QR decomposition. The network is used to identify the converter parameter values running out of tolerance. This technique is applied in the fault detection of parameters of a Buck DC-DC PWM converter and is based on steady...
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