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Automatic Neural Model Development for Power Amplifier. Na Weicong. Content. Example: Power Amplifier. Problem & Solution. Comparison & Conclusion. Automatic Neural-Network Structure Adaptation with Interpolation Approaches. Add training data
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Automatic Neural Model Development for Power Amplifier Na Weicong
Content Example: Power Amplifier Problem & Solution Comparison & Conclusion
Automatic Neural-Network Structure Adaptation with Interpolation Approaches Add training data & test data Yes Interpolation Approaches n, j Overlearning ? Training & Test Training & Test Add a hidden neuron Stop Goodlearning n-1, j Stop Add a hidden neuron Goodlearning Yes No Underlearning Underlearning Has it been Trained before? Add a hidden neuron No
Example: MOSFET vs. Power Amplifier Id ··· Vgs Vds Pin= -5~+5 dBm Vdin= 2~3 V RL= 50~60 f= 2.1~2.8 kHz Vgs= 0~4 V Vds= 0~4 V
Interpolation Algorithm • Select the type of interpolation formula. Linear Function, 2nd order Polynomial Function etc. • Select the points which can represent the interpolation region. These points are always the boundary points of the region. • Calculate the equation to obtain the parameters in the interpolation equation. • Substitute the coordinates of the interpolated point into the interpolation equation whose parameters we have known, then we will get the final result.
Step1: Select the type of interpolation formula. MOSFET:2nd order polynomial function Power Amplifier: 3rd order polynomial function
MOSFET:2nd order polynomial function Power Amplifier: 3nd order polynomial function k: the number of samples
Step2: Select the points which can represent the interpolation region. MOSFET: k =5+4=9 6 4 5 2 4 0 64 0 2 4 Power Amplifier: k =64+16=81 16 19
Step3: Calculate the equation to obtain the parameters in the interpolation equation. Problem: matrix is a singular matrix! Solution: Change 3rd order polynomial function!
Comparison(Example:Power Amplifier) *tested by the same test data