# Fuzzy Neural Networks--A Review with Case Study
## 2 Fuzzy Neural Network Description
### Papers Review on Defined Criteria
The approach delivers better performance for the proposed solutions compared to traditional convolutional neural networks. In [33], the authors developed a thermal control strategy to stabilize hydrogen production by improving temperature control precision during the electrolysis process. This was achieved by adding a fuzzy controller as an additional layer to a Long Short-Term Memory (LSTM) network. In [34], an ARIMA (autoregressive integrated moving average) model combined with a neural network for membership function utilization was proposed for predicting business cycles. In [35], the authors proposed a fuzzy BAM neural network that was a discrete-time, fractional-order, complex-valued system. Achieving quasi-projective synchronization of the proposed system required developing a quantitative control strategy. In [36], the authors developed a fuzzy neural network for breast cancer prediction. This fuzzy network achieved accuracy, sensitivity, and specificity all above 99%. However, it required the definition of fuzzy rules and membership functions. The testing was conducted in a Matlab environment. In [37], the authors introduced a fuzzy neural network for controlling the position of an unmanned aerial vehicle. Flight data captured from a real vehicle was used during testing. For the position control loop, an adaptive tuning method based on a fuzzy neural network-compensated proportional-integral-derivative (PID) controller was employed, allowing online adjustments. This approach leveraged the strengths of both fuzzy systems and neural networks while reducing the computational complexity typically associated with cascade neural network control. In [38], a fuzzy neural network PID controller was used to reduce the vibration levels of helicopters, achieving up to a 60% reduction in vibration. In [39], the authors proposed a fuzzy neural network PID control strategy optimized by Particle Swarm Optimization (PSO) for managing pH levels in water and fertilizer integration systems. Their approach combined a fuzzy preprocessing controller with a neural network PID controller. The experiments were carried out using MATLAB/SIMULINK software. In [40], a fuzzy neural network PID controller was utilized as an automatic speed regulator for a motor system. A fuzzy single-neuron neural network was applied for this purpose and the proposed fuzzy controller achieved better results compared to traditional methods. Such fuzzy neural network PID solutions have also been used for active suspension control in vehicles [41]. In [42], the authors proposed a solution for a servo drive system. They used a recurrent fuzzy neural network combined with backstepping control. This approach enabled the motor to adapt to nonlinear changes in behavior during operation. In [43], a fuzzy neural network was utilized to assess the signal quality indices of photoplethysmography pulses obtained via impedance cardiography. In [44], a fuzzy neural network was applied to predict k-barriers for intrusion detection in wireless sensor networks. This network effectively addressed data stream regression problems while maintaining a high level of interpretability. In [45], a fuzzy neural network was applied in a mobile robot as a control mechanism for trajectory tracking. This network utilized a self-organizing algorithm that included rule growth. A similar problem was addressed in [46], where a fuzzy neural network was used for the same purpose. This solution incorporated Kalman filters. In [47], a fuzzy neural network was used as a motion compensator for gantry robots to correct synchronous errors between dual servo motors, improving precision in movement. In [48], a fuzzy neural network was utilized for rotor fault diagnosis, achieving an average recognition accuracy exceeding 99%, outperforming other solutions. However, the definition of membership functions was necessary. In [49], a type-2 fuzzy neural network was used for nonlinear system control. The operation of the network required the definition of fuzzy rules.