Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks
β Scribed by Jianfeng Zhai; Jianyi Zhou; Lei Zhang; Wei Hong
- Book ID
- 118248757
- Publisher
- IEEE
- Year
- 2010
- Tongue
- English
- Weight
- 294 KB
- Volume
- 20
- Category
- Article
- ISSN
- 1531-1309
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract A radialβbasis function neural network (RBFNN) approach is proposed for predicting the nonlinear behaviors of gallium nitride (GaN) Doherty amplifier. Sampled input and output data from a designed GaN Doherty amplifier were used to train and test the proposed RBFNN model. Comparison of
This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers (PAs), including strong nonlinearities and memory effects. Feedforward time-delay Neural Networks (TDNN) are used to extract the model from a large-signal input-output time-do
In digital radio systems, high data transmission rates require the use of spectrally efficient linear modulation techniques; however, these techniques are generally sensitive to nonlinearity caused by the high-power amplifier (HPA) employed in transmitter systems. The nonlinearity of HPA is potentia