## 1 Introduction and Initial Considerations
### Motivation and Argumentation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. No.** & **Control Logic** & **Comment** & **Contribution** \\ \hline \multirow{4}{*}{[17]} & Intelligent real-time controller based on OENN and OENNFIS & The structure uses offline and online learning approaches simultaneously. Additionally, two different types of glands are used: excitatory and inhibitory. & Stable control of nonlinear multiple-input-multiple-output dynamic systems influenced by high-intensive environmental disturbances. \\ \hline \multirow{4}{*}{[18]} & Nonlinear autoregressive neural network (NARX) with activation functions based on orthogonal polynomials & Standard activation functions within the network were substituted with newly developed orthogonal polynomial functions. & A method for improvement of magnetic levitation performances in terms of achieving specified levitation amplitude. \\ \hline \multirow{4}{*}{[19]} & Orthogonal endocrine neural network based on postsynaptic potentials & Artificial inhibitory and excitatory processes are proposed to mimic the self-adjusting biological processes of keeping desired parameters within optimal and specified values. & The architecture is suitable for data forecasting purposes. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._and identification of MLS. The controller consisted of three parts: PID controller, radial basis function network controller and radial basis function network identifier. On the other hand, a synergistic combination of neural network and sliding mode control was proposed in [22]. Justification for introducing and using this complex, intelligent structure can be found in the following considerations. Generally speaking, artificial neural networks possess the capability of a precise generalization and show robustness when dealing with disturbances. However, they can have difficulties coping with uncertainties, providing interpretability and identifying the importance of each input signal and environmental stimuli for a related system. The last-mentioned deficiency is one of the crucial reasons for considering the OENN + AOG structure for implementation. If an examined dynamical system is linear or simple nonlinear, it cannot be advised to apply a novel structure. Additionally, if there is a lack of data for building a data set, available information is limited or environmental stimuli are not measurable, it is also better to utilize another simpler intelligent or traditional controller. It is justified to use the proposed OENN + AOG structure when it is required to control a complex nonlinear dynamical system when traditional control logics do not provide satisfying results. Particular interest in using the architecture is when numerous measurable environmental stimuli and disturbances exist and can be sensed. The structure's benefit is in introducing an unlimited number of such stimuli to the gland network and later to a designed OENN. It was experimentally shown in the previous section that the novel structure can provide representative results (on the level of the similar complex smart solution) but with significantly less computation time. All acquired and processed environmental stimuli provide a significant value to a connected neural network and controlled system. They can help in training free parameters if treated correctly according to the current environmental conditions.