Search

Summary: Recurrent neural networks (RNN) emerged as powerful tools to model and analyze the nonlinear behavior of electronic circuits accurately and quickly. Efforts to improve the accuracy of RNN will lead to the design of better‐quality products, which is essential in various fields such as the design of energy harvesting (EH) systems. EH techniques can provide the electrical energy needed for low‐power electronics without the need for a battery or with minimal dependency. Due to the importance of the active voltage balancing circuit in EH systems, we have proposed a new macromodeling method called dropout local‐feedback deep recurrent neural network (Dropout‐LFDRNN) to model and analyze this circuit along with two other nonlinear circuits as examples. This technique is an advance over the LFDRNN macromodeling method, and based on the obtained results from the measurements, we were able to build a fast macromodel for the active balancing circuit, which outperforms conventional deep recurrent neural network modeling method in terms of accuracy without sacrificing speed. It is worth mentioning that this proposed technique in this paper can be considered a viable approach for modeling and analysis of nonlinear electronic components and circuits. In addition to the advantage of generating a more accurate model, the model based on the Dropout‐LFDRNN approach is much faster than the existing transistor‐level models. [ABSTRACT FROM AUTHOR]

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]

In this study, a framework for statistical design of a brushless DC (BLDC) motor is proposed in order to maximise yield while keeping efficiency as high as possible and the non‐linear constraints, that is, limitations of flux density, current density and physical dimensions, satisfied. The proposed yield maximisation method has two major steps: polyhedral approximation of the constraint region and yield maximisation. By implementing the proposed method, the optimum design parameters, that is, embrace, thickness and length of magnet, are obtained with maximum immunity to variations in magnet dimensions. Also, highly accurate artificial neural network‐based models are used to estimate the objective function and constraints. It is also shown that by adjusting performance constraints (efficiency and flux density), different design goals can be achieved which help to choose the appropriate design according to the designer's goals. [ABSTRACT FROM AUTHOR]

Here, the authors present a new constrained multi‐objective optimization method for maximizing output power and efficiency of switched reluctance motor (SRM). The constraints play a significant role in this process. Artificial neural network‐based models are created to represent the SRM behaviour which is a difficult task. For this reason, a single neural network for each objective is built and the number of training data has been increased to reach a good accuracy. The authors propose a neural network‐based adjoint Lp metric technique to combine several objective functions and constraints with different weight factors to a single function which should be minimized. To apply the constraints, an adjoint term is added to the original Lp function including only objective functions. The flux densities of the magnetic materials are selected as the optimization constraints. When approaching as close as possible to the saturation level, the SRM has a better performance. Adding an adjoint term to the original Lp leads to better optimization results which were proved by comparing with a conventional optimization. Also, all obtained results were finally validated by the Ansoft Maxwell software. [ABSTRACT FROM AUTHOR]

This article presents a new macromodeling approach for nonlinear electronic components and circuits based on long short-term memory (LSTM) neural network. LSTM proposes a more efficient training process in comparison with the conventional recurrent neural network (RNN) training. Conventional structures such as RNN suffer from the gradient vanishing problem during the training process. LSTM addresses this issue and solves this problem in an efficient way. In order to train the proposed structure, some input and output waveforms of the original circuit, called training waveforms, should be obtained from simulation tools or measurements. Model creation using proposed method does not require information on details inside the components and input–output training waveforms are sufficient to construct the model. The provided numerical results in this article show that the proposed method is more efficient than RNN techniques for modeling components and packages in terms of both speed and accuracy. The findings suggest that the proposed method significantly reduces the training time in comparison with conventional state-of-art modeling techniques. Furthermore, simulation time of the obtained model from the proposed technique is less than both conventional models (such as SPICE models) used in circuit simulation tools and models obtained from conventional RNN method. Three practical examples, namely, an audio amplifier, Texas Instruments (TIs) SN74AHCT540 device, and MOS inverter, are utilized to manifest the validity of the proposed macromodeling approach. [ABSTRACT FROM AUTHOR]