## 1 Introduction
This study addressed the problem of selecting learning rate and weight distribution parameters that affect the proposed self-organizing fuzzy controller (SOFC) performance in engineering problems. For this reason, a hybrid self-organizing fuzzy and radial basis functional neural network controller (HSFRBNC) was proposed in this study. SOFC and HSFRBNC methods were compared, and HSFRBNC was found to provide better control performance in improving the suspension system lifetime and ride comfort of a car. [PERSON] et al. [15] applied Ho control for active car suspension systems in the finite frequency domain with time domain constraints to the model of a quarter car in a simulation environment. In this study, using the generalized Kalman-Yakubovich-Popov (KYP) lemma, a state feedback controller was designed in linear matrix inequality (LMI) optimization in Ho-norm, leading to controlled output without distortion. Reduced results were obtained in a certain frequency band to improve driving comfort. [PERSON] et al. [16] proposed a reliable fuzzy Ho control method for actuator-delayed and faulty ASSs and applied it in a simulation environment. A passive system and an FLC method were contrasted with the approach. The simulation results showed that the designed reliable fuzzy controller could provide better suspension performance in the presence of changes in sprung and unsprung mass, delay, and actuator failure. [PERSON] et al. [17] proposed an adaptive rollback control strategy for car suspensions with rigid conditions. The proposed controller considers suspension spacing, dynamic tire loads, and actuator saturation as time boundary conditions. In the presence of parameter uncertainties, the method was applied to the half-car model in a simulation environment in the study to stabilize vehicle performance and improve ride comfort. [PERSON] et al. [18] proposed a limited adaptive back-stepping control strategy for ASSs to achieve multi-purpose control. In addition to improving ride comfort, the time-domain constraints required for active suspension control were also guaranteed throughout the time domain and the Lyapunov barrier function was used in this study. The method was applied to the model of a quarter vehicle. [PERSON] et al. [19] proposed a disturbance observer-based sliding mode control method for ASSs and implemented it in a simulation and experimental environment. In the study, three different types of road profiles and load variations were tried and verified. The results were compared with those of passive suspension systems, significantly improving mass displacement and acceleration performance. To shed light on the design of suspension control systems in the existing literature, [PERSON] and [PERSON] offered a literature study [20] in which they shared their observations on the most recent hardware implementations of active and semi-ASSs. [PERSON] et al. [21] proposed a static output feedback control strategy based on the variable substitution method and linear matrix inequality for ASSs with limited information. The main feature of the proposed method was that it provided a pre-assignment of the controller structure. This study, which used a half-vehicle model, aimed to improve ride comfort and balance simultaneously. The validity of the designed controller for different track profiles was shown by numerical examples, taking into account other boundary conditions such as suspension preload, actuator saturation, controller-dependent information, etc. Simulation results showed that the optimized controller with static output feedback achieved better suspension performance than a suitable controller with static output feedback. [PERSON] and [PERSON] proposed a fuzzy logic control method for controlling ASSs and applied it to a quarter-vehicle model in a simulation environment [22]. The system was compared with a passive and a PID-controlled system. The FLC method was applied to three road profiles and positive results were obtained. [PERSON] et al.