Neural network based PLL
AssigneeNational Semiconductor Corporation
InventorWilliam Shields Neely
Life of this patent
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Abstract
A neural network controlled phase lock loop (PLL) with an artificial neural network for comparing the phase of the feedback signal from the voltage controlled oscillator (VCO) with the phase of the PLL reference signal. The VCO feedback signal and PLL reference signal are inputted to and processed by a multiple layer perception which has been trained with a model of the VCO to generate the appropriate control voltage necessary to establish and maintain the desired phase and/or frequency of the VCO output signal.
Claims34
An apparatus including a neural network controlled oscillator loop comprising:
- a neural network signal processor configured to receive and process a reference signal and an oscillator signal and in accordance therewith provide a control signal, wherein said reference signal includes a reference phase and a reference frequency and said oscillator signal includes an oscillator phase and an oscillator frequency;
- and
- an oscillator, coupled to said neural network signal processor, configured to receive said control signal and in accordance therewith provide said oscillator signal, wherein at least one of said oscillator phase and frequency is controlled by said control signal and is established in accordance with at least one of said reference phase and frequency, respectively.
A neural network system which includes a neural network signal processor and is configured to train said neural network signal processor for controlling an oscillator, said neural network system comprising:
- a neural network signal processor configured to receive and back propagate an error signal and to receive and process a desired response signal and an example input signal and in accordance therewith provide a control signal, wherein said error signal corresponds to a difference between said desired response signal and said example input signal;
- an inverse circuit model, coupled to said neural network signal processor, configured to receive said desired response signal and in accordance therewith provide an inverse model signal, wherein said inverse circuit model has a transfer function associated therewith and a ratio of said control signal and said example input signal correspond to an inverse of said transfer function;
- and
- an error detection circuit, coupled to said neural network signal processor and said inverse circuit model, configured to receive and process said control signal and said inverse model signal and in accordance therewith provide said error signal.
A neural network based method of controlling an oscillator comprising the steps of:
- receiving and processing a reference signal and an oscillator signal and in accordance therewith generating a control signal with a neural network signal processor, wherein said reference signal includes a reference phase and a reference frequency and said oscillator signal includes an oscillator phase and an oscillator frequency;
- and
- receiving said control signal and in accordance therewith generating said oscillator signal, wherein at least one of said oscillator phase and frequency is controlled by said control signal and is established in accordance with at least one of said reference phase and frequency, respectively.
A method of training a neural network signal processor for controlling an oscillator, said method comprising the steps of:
- receiving and back propagating an error signal with a neural network signal processor;
- receiving and processing a desired response signal and an example input signal and in accordance therewith generating a control signal with said neural network signal processor;
- receiving said desired response signal and generating an inverse model signal in accordance with a transfer function, wherein a ratio of said control signal and said example input signal correspond to an inverse of said transfer function;
- and
- processing said control signal and said inverse model signal and in accordance therewith generating said error signal, wherein said error signal corresponds to a difference between said desired response signal and said example input signal.
Dependencies are inferred from the claim text.