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4. High-Speed Nonlinear Circuit Macromodeling Using Hybrid-Module Clockwork Recurrent Neural Network

Author

Charoosaei, Fatemeh, Faraji, Amin, Sadrossadat, Sayed Alireza, Mirvakili, Ali, Na, Weicong, Feng, Feng, and Zhang, Qi-Jun

Abstract

In the computer-aided design (CAD) area, the recurrent neural network (RNN) has shown notable functionality in generating fast and high-performance models rather than the models in simulation tools. Predicting time sequences is a pervasive and challenging problem that may require identifying the dependencies between sequences that RNN is capable of performing. Despite all its features, conventional RNN still faces challenges such as limited accuracy and a large number of parameters. Therefore, we propose new macromodeling methods for nonlinear circuits called the Clockwork-RNN (CWRNN) and its hybrid version which is a more powerful but simpler implementation of a conventional RNN architecture with relatively little model complexity. In addition, CWRNN inherently models complex dependencies without the need for a large number of parameters. As a result, the computational cost is less than conventional RNN. Moreover, understanding and implementing the CWRNN is relatively simple and provides great flexibility in architectural configuration by introducing modules with several clock rates of exponents of 2. In addition to the above new modeling technique, we proposed the Hybrid-Module CWRNN as another new modeling method that utilizes modules of various exponents of different numbers resulting in further accuracy improvement of the CWRNN. Furthermore, the models obtained from the proposed techniques required much smaller simulation times compared to the current models used in simulation tools. Three nonlinear high-frequency examples have been utilized to verify the benefits of the proposed modeling methods.

Published
2024
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13. A framework for statistical design of a brushless DC motor considering efficiency maximisation.

Author

Sadrossadat, Sayed Alireza and Rahmani, Omid

Subjects
*BRUSHLESS electric motors, *ARTIFICIAL neural networks, *ENERGY consumption, *QUANTITATIVE research, *ACTINIC flux
Abstract

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]

Published
2022
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15. Optimization of efficiency and output power of 8/6 switched reluctance motor using new neural network‐based adjoint Lp metric method.

Author

Rahmani, Omid, Sadrossadat, Sayed Alireza, Mirimani, Seyyed Mehdi, and Mirimani, Seyyed Hossein

Subjects
*ARTIFICIAL neural networks, *RELUCTANCE motors, *BRUSHLESS electric motors, *MAGNETIC flux density, *TORQUE control
Abstract

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]

Published
2021
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