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Summary: In this paper, a novel method is presented for dynamic behavioral modeling of nonlinear circuits. The proposed adjoint recurrent neural network (ARNN) model is an extension of the existing recurrent neural network (RNN) technique which adds derivative information to the training data set. This addition makes training more efficient while using fewer data in comparison with the conventional RNN method with the same accuracy. Also, formulation of proposed ARNN model makes it suitable for parallel computation. Therefore, the proposed technique makes the training process much more efficient than RNN by using derivative information and parallelization. Additionally, the proposed model is much faster compared to conventional models present in existing simulation tools. The validity and accuracy of the proposed model is illustrated through macromodeling of a commercial NXP's 74LVC04A device and a five‐stage complementary metal‐oxide semiconductor (CMOS) receiver device. [ABSTRACT FROM AUTHOR]

Balancing the voltage of series connected supercapacitors is a necessity. Various passive and active balancing techniques are reported for alleviating the problems of leakage and overvoltage. In this paper, a novel active balancing approach based on boost converter is presented leading to the implementation of a piezoelectric energy harvesting (EH) system. Besides, this nonlinear boost converter is designed, implemented, and modeled using a new macromodeling approach. In this regard, data measured by the implemented boost converter passed through local feedback deep recurrent neural networks (LFDRNNs), in order to model the nonlinear behavior of this converter, and this model can be used to design the EH system. LFDRNN can be trained directly using the input–output waveform samples of the main circuit without knowing its internal details, and the obtained model has similar accuracy compared to the original circuit. The main focus of this paper is the new LFDRNN macromodeling method which is associated with the boost converter‐based active balancing technique. Our experimental results show that LFDRNN extends the ability of conventional neural network‐based models to express the dynamic behavior of nonlinear circuits while increasing the accuracy. Additionally, LFDRNN‐based models are much faster than existing models in simulation tools. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new method called local‐global feedback recurrent neural network (LGFRNN) is proposed for dynamic behavioral modeling of nonlinear circuits. The structure of the proposed method is based on recurrent neural network and constructed by time‐delayed local and global feedbacks. Adding time‐delayed feedbacks has a great impact on the learning capability of previous neural network‐based methods. Moreover, time‐delayed local feedbacks alleviate the problem of slow convergency of the conventional neural network‐based methods in the training phase. The proposed LGFRNN can be trained only by having sampled input‐output waveforms of the original circuit without knowing the internal details of the circuit. A training algorithm based on real‐time recurrent learning (RTRL) is used to train LGFRNN. After the training phase, the proposed LGFRNN provides accurate macromodel of a nonlinear circuit. The proposed method is more accurate compared with the conventional neural‐based models (which do not benefit from time‐delayed local‐global feedbacks) and also significantly reduces the training time of the conventional models. Moreover, proposed LGFRNN is faster than the existing models in simulation tools. The validity of the proposed method is verified by time‐domain modeling of three nonlinear devices including commercial TI's SN74AHCT540 device, five‐stage complementary metal‐oxide‐semiconductor (CMOS) receiver, and commercial TI's LM324 power amplifier. [ABSTRACT FROM AUTHOR]