## 4 Conclusions  
This paper examines five instances of machine learning (ML) applications to the FSM method. For each use case, the requisite feature sets, learning type, benefits, and potential issues are analyzed. A summary is presented in Table 10.  
The initial use case is parametric circuit synthesis, in which a circuit can be synthesized using the estimated automatically through reinforcement learning. The primary challenges are the time-consuming process and the necessity for a substantial database for ML experience storage. The second use case pertains to the detection of new features, which involves identifying features of the FSM method or its implementation that are not visible or cannot be discerned from specific cluster occurrences. This use case employs unsupervised learning, but it is currently unclear which clusters could be obtained. The third use case is prediction, where results can be predicted based on previous experience without the need for modeling and simulation. This use case employs supervised learning, but the result may be incorrectly predicted for atypical inputs. The fourth use case involves an assistant that could detect human mistakes through unsupervised learning. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Case** & **Benefit** & **Learning Type** & **Difficulties/Problems** \\ \hline \multirow{3}{*}{Parametric circuits synthesis} & Circuit could be synthesized & \multirow{3}{*}{Reinforcement learning} & Could be time consuming. \\  & automatically. & & Big database is needed for ML \\ \hline \multirow{3}{*}{New features detection} & Identify not visible features of & \multirow{3}{*}{Unsupervised learning} & At the moment, it is not clear \\  & the FSM method or its & & which clusters could be \\  & implementation by specific & & obtained. \\  & clusters occurrences. & & \\ \hline \multirow{3}{*}{Prediction} & Could predict the result based & \multirow{3}{*}{Supervised learning} & For some not typical inputs, \\  & on previous experience & & the result could be predicted \\ \cline{1-1}  & without modeling and & & incorrectly. \\ \hline \multirow{3}{*}{Anomaly detection} & Human mistakes could be & \multirow{3}{*}{Supervised learning} & Could accidentally report an \\  & detected. This use case acts as & & anomaly when the correct \\ \cline{1-1}  & an assistant. & & approach is used. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Use cases summary. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & **Number of Operations** & **Cost** \\ \hline add & 240 & 240 \\ sub & 238 & 238 \\ mul & 433 & 1299 \\ div & 5 & 35 \\ exp & 373 & 23,872 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Result of transfer function complexity calculation using FSM for 2 harmonics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & **Number of Operations** & **Cost** \\ \hline add & 722 & 722 \\ sub & 626 & 626 \\ mul & 1213 & 3639 \\ div & 7 & 49 \\ exp & 1081 & 69,184 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Result of transfer function complexity calculation using FSM for 3 harmonics. However, there was a potential problem of inadvertently reporting an anomaly when the correct approach was used. The fifth use case was fault detection, which allowed the identification of faults not detected by standard functional tests. This use case used supervised learning but required a sufficient circuit model (simulation). There are other issues that must always be properly addressed, such as data preparation, model selection, training, and evaluation, with the aim of improving the overall efficiency of the diagnostic system, but these are all circuit-dependent and must be chosen by a test designer, engineer, or AI expert. Therefore, there is no single or best solution for a particular test case, so we have proposed a set of real-world applications that should reduce the time taken to develop a single test for a custom circuit. Conceptualization, Y.S. and D.G. ; Methodology, Y.S. and D.B. ; Software, S.M. ; Validation, S.M. and L.C.