Systems and methods for controlling frequency output of an electronic oscillator to compensate for effects of one or more parameters experienced by the oscillator incorporate artificial neural network processing functionality for generating correction signals. A neural network processing module includes one or more neurons which receive one or more inputs corresponding to parameters of an electronic oscillator, such as temperature and control voltage (or correction voltage). One or more sets of weights are calculated and applied to inputs to the neurons of the neural network as part of a training process, wherein the weights help shape the output of the neural network processing module. The neural network may include a linear summation module configured to provide an output signal that is at least partially based on outputs of the one or more neurons.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of controlling frequency output of an electronic oscillator to compensate for effects of parameters experienced by the oscillator, the method comprising: receiving first test data and second test data from an electronic oscillator, the electronic oscillator being subject to first and second parameters, wherein the first test data corresponds to outputs generated by the electronic oscillator while being subject to at least the first parameter over a first range of values, and the second test data corresponds to outputs generated by the electronic oscillator while being subject to at least the second parameter over a second range of values, and wherein the first parameter is related to a temperature of at least a portion of the electronic oscillator and the second parameter is related to a correction voltage provided to the electronic oscillator; calculating a first plurality of weights based at least in part on the first test data; calculating a second plurality of weights based at least in part on the second test data; receiving from the electronic oscillator one or more input signals corresponding to current values of the first and second parameters; determining, by one or more neural network processing modules comprising one or more processors, a neural network output signal based at least in part on the one or more input signals and the first and second pluralities of weights; determining the correction voltage based at least in part on the neural network output signal; and providing the correction voltage to the electronic oscillator to at least partially control the frequency of the electronic oscillator. 2. The method of claim 1, wherein said calculating the first plurality of weights and said calculating the second plurality of weights are performed prior to said receiving the one or more input signals. 3. The method of claim 1, further comprising generating an intelligent seed value, wherein at least one of said calculating the first plurality of weights and said calculating the second plurality of weights is based at least in part on the seed value. 4. The method of claim 1, wherein calculating the neural network output signal comprises: providing the one or more input signals and the first and second pluralities of weights to a plurality of artificial neuron modules of the neural network processing module, wherein at least one of the first plurality of weights and at least one of the second plurality of weights are provided to each of the plurality of artificial neuron modules; and providing a neuron output from each of the plurality of neuron modules to a linear summer, wherein the neural network output signal is based at least in part on an output of the linear summer. 5. The method of claim 4, further comprising providing a bias input to the linear summer for shifting the output of the linear summer up or down. 6. The method of claim 4, wherein each of the plurality of artificial neuron modules utilizes at least one of the plurality of weights and each of the one or more input signals as variables of a non-linear activation function, wherein each of the plurality of artificial neuron modules provides an output that is based at least in part on a solution of the non-linear activation function. 7. The method of claim 6, wherein the activation function is a sigmoid function. 8. The method of claim 1, wherein calculating the neural network output signal comprises: providing the one or more input signals and the first plurality of weights to a first plurality of artificial neuron modules of a first neural network processing module of said one or more neural network processing modules, wherein at least one of the first plurality of weights is provided to each of the plurality of artificial neuron modules; providing the one or more input signals and the second plurality of weights to a second plurality of artificial neuron modules of a second neural network processing module of said one or more neural network processing modules, wherein at least one of the second plurality of weights is provided to each of the second plurality of artificial neuron modules; and providing a neuron output from each of the first plurality of neuron modules to a first linear summer and a neuron output from each of the second plurality of neuron modules to a second linear summer, wherein the neural network output signal comprises a first neural network output signal based at least in part on an output of the first linear summer and a second neural network output signal based at least in part on an output of the second linear summer. 9. A system for controlling frequency output of an electronic oscillator, the system comprising: a first input signal corresponding to a first parameter of an electronic oscillator; a second input signal corresponding to a second parameter of the electronic oscillator; and a neural network processing module operatively coupled to the first and second input signals, the neural network processing module comprising a plurality of artificial neuron modules, wherein each of the plurality of artificial neuron modules is configured to receive the first and second input signals and one of one or more pre-calculated weights and provide an output signal, for use in controlling the frequency output of the electronic oscillator, based at least in part on the first and second input signals and the one of the one or more pre-calculated weights, wherein the one or more pre-calculated weights are derived at least in part from test data that corresponds to outputs generated by the electronic oscillator while experiencing a first range of values of the first parameter and a second range of values of the second parameter. 10. The system of claim 9, wherein the first parameter is related to a temperature of at least a portion of the electronic oscillator. 11. The system of claim 9, wherein the second parameter is related to a correction voltage provided to the electronic oscillator, the correction voltage configured to at least partially control the frequency output of the electronic oscillator. 12. The system of claim 9, further comprising a third input signal corresponding to a third parameter, wherein each of the plurality of artificial neuron modules receives the third input signal, the output signal being based in part on the third input signal. 13. The system of claim 9, wherein the neural network processing module provides the output signal to a voltage conversion module configured to convert the output signal into a correction voltage and provide the correction voltage to the electronic oscillator. 14. A system for providing a correction signal to at least partially compensate for effects of parameters of an electronic device, the system comprising: a first neural network processing module comprising one or more processors and configured to receive a first input signal corresponding to a first parameter; and a second neural network processing module comprising one or more processors and configured to receive the first input signal and a second input signal corresponding to a second parameter; wherein each of the first and second neural network processing modules comprises a plurality of artificial neuron modules, wherein each of the plurality of artificial neuron modules receives one of one or more pre-calculated weights, wherein the first neural network processing module is configured to provide a first output signal based at least in part on the first input signal and the one or more pre-calculated weights, and wherein the second neural network processing module is configured to provide a second output signal based at least in part on the first and second input signals and the one or more pre-calculated weights. 15. The system of claim 14, further comprising one or more voltage conversion modules configured to convert the first and second output signals into first and second correction signals, respectively. 16. The system of claim 15, further comprising a summation module configured to generate a third correction signal based on the first and second correction signals and provide the third correction signal to the electronic device. 17. The system of claim 14, wherein the first input signal corresponds to a temperature of at least a portion of the electronic oscillator. 18. The system of claim 14, wherein the second input signal corresponds to a correction voltage provided to the electronic oscillator, the correction voltage configured to at least partially control the frequency output of the electronic oscillator. 19. The system of claim 14, further comprising a memory module, an analog-to-digital converter, a digital-to-analog converter, and a low-pass filter. 20. The system of claim 19, wherein the memory module is configured to store one or more values calculated by multiplying each of the one or more pre-calculated weights by a slope control variable, and store one or more values calculated by multiplying one or more bias inputs provided to the first and second neural network processing modules by the slope control variable.