Akademik Veri Yönetim Sistemi
Journal of Dynamics and Games, cilt.11, sa.3, ss.232-248, 2024 (ESCI)
Symbolic simplification of analog circuits is a computationally challenging problem due to the exponential growth of the number of symbolic terms with the circuit size. In recent years, researchers have proposed various methods to address this problem, but these methods often require matrix- or graph-based symbolic analysis methods, which can be computationally expensive and memory-intensive, especially for real-size analog circuits. To overcome these limitations, we introduce a new methodology called Direct Simplified Symbolic analysis (DSSA). The proposed DSSA method views simplified symbolic circuit analysis as a modeling problem and directly produces the most significant terms of the transfer function, without the need for traditional circuit analysis. One of the main advantages of DSSA is that it significantly reduces computational complexity and required memory compared to the existing techniques. This is achieved by generating a dataset using the Monte-Carlo simulation method and performing a genetic algorithm to solve the established modeling problem. The objective is to minimize the average numerical error between the simplified symbolic expression and the exact numeric expression for all data points. The proposed method has been tested on five circuits in MATLAB, and the results clearly demonstrate its performance against existing methods. The findings by the DSSA algorithm across five circuits reveal 0.64 dB and 1.36 dB variations for the average and maximum dc-gain, respectively. Moreover, the DSSA algorithm exhibits an average pole/zero error of 6.8% and a maximum pole/zero displacement of 16.8%. It has the potential to improve the efficiency and accuracy of symbolic analysis, making it a promising tool for circuit designers and engineers.