## 7 Discussion
### Computational Complexity
\begin{table}
\begin{tabular}{c l l l l} \hline
**Method/Reference** & **Method Description** & **Computational Complexity** & **Key Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Current Proposal} & T-S identification with membership functions & Low, thanks to the simplicity of the formula and the efficiency in computing distances & High accuracy, lower computational cost, differentiable, and easy to implement in real time & May be less flexible for certain types of complex nonlinear data \\ \hline \multirow{3}{*}{[3]} & UKF and dual estimation for T-S identification & High (use of UKF can increase complexity) & Higher accuracy in highly nonlinear systems, suitable for complex dynamics & Intensive use of matrix calculations, which can be computationally expensive \\ \hline \multirow{3}{*}{[4]} & T-S identification with quadratic cost minimization & Medium (use of quadratic optimization) & Good transient response and system stability & Requires adjustments and computation time for quadratic optimization \\ \hline \multirow{3}{*}{[5]} & Optimization of approximation with parameter weighting & Medium-high (optimization with weighting) & Robust dynamic response and zero steady-state error & Greater complexity in selecting weights \\ \hline \multirow{3}{*}{[7]} & Evolving T-S models for identification & Medium (efficiency in input space decomposition) & Improved accuracy and computational efficiency & May require fine tuning of parameters \\ \hline \multirow{3}{*}{[8]} & State-space subspace in T-S identification & High (subspaces can be complex) & Combines T-S with subspace techniques for robust identification & Intensive use of subspace algorithms can be costly \\ \hline \multirow{3}{*}{[9]} & T-S system based on FCRM models and clustering & Medium (use of clustering and FCRM) & Easy construction of fuzzy systems with experimental data & May be less efficient for large datasets \\ \hline \multirow{3}{*}{[10]} & Data-driven methodology for T-S models & Low (fewer linearizations and high precision) & High accuracy and control with lower computational cost & May require more parameter tuning time \\ \hline \multirow{3}{*}{[11]} & Modified Gath-Ceva clustering for T-S optimization & Medium (clustering & Higher accuracy in nonlinear system identification & Additional complexity in clustering optimization \\ \hline \multirow{3}{*}{[12]} & Clustering techniques for constructing T-S rules & Medium (interpolation and clustering) & Improved prediction accuracy & Complex interpolation and potential scalability issues \\ \hline \multirow{3}{*}{[14]} & T-S models with clustering algorithms & Medium (clustering & Improved generalization and accuracy in dense regions & Complexity in parameter optimization \\ \hline \multirow{3}{*}{[15]} & Quadcopter identification with T-S and backpropagation & Low (use of only four membership functions) & High accuracy and efficiency with low computational cost & May be less suitable for more complex systems \\ \hline \multirow{3}{*}{[16]} & T-S identification for HIV dynamics & Medium (use of Gaussian functions) & High accuracy in describing HIV infection dynamics & Less efficient for systems outside the HIV context \\ \hline \multirow{3}{*}{[17]} & Identification with T2-ETS systems & Medium (balance between complexity and precision) & Balance between complexity and prediction accuracy & May require additional complexity adjustments \\ \hline \multirow{3}{*}{[18]} & Combined method for Q-function in T-S & High (use of LMI and Q-function adjustments) & Reduction in convergence issues and optimization improvement & Intensive use of LMI techniques can increase complexity \\ \hline \multirow{3}{*}{[24]} & Neuro-fuzzy T-S models for nonlinear systems & High (neuro-fuzzy model can be complex) & High accuracy and robustness without complete system information & Complexity in integrating neural networks and fuzzy logic \\ \hline \multirow{3}{*}{[25]} & Stochastic configuration networks fuzzy T-S & High (improvements in inference capability) & Significant improvement in inference capability and identification accuracy & Added complexity in stochastic configuration \\ \hline \multirow{3}{*}{[26]} & Type-2 neural networks T-S for identification & High (advanced handling of uncertainty) & Better handling of uncertainty and improved modeling accuracy & Complexity in designing and tuning type-2 networks \\ \hline \end{tabular}
\end{table}