# Fuzzy Neural Networks--A Review with Case Study
## 2 Fuzzy Neural Network Description
### Papers Review on Defined Criteria
In [50], such a network, combined with a swarm optimization algorithm, was used for control systems and data prediction. In [51], the authors developed a Taguchi-based multiscale convolutional compensatory fuzzy neural network model for automatic sleep stage detection and classification. In this solution, the data comes from electroencephalogram signals. The proposed approach used a fuzzy neural network for data classification and achieved a better learning convergence rate with a lower number of model parameters. In [52], a type-2 fuzzy neural network with a Gaussian regression model was used for high-resolution image classification. In [53], a fuzzy neural network was utilized for traffic flow prediction, which also required defining fuzzy rules and membership functions. In [54], a fuzzy neural network was applied for photovoltaic failure analysis. The type-2 fuzzy neural network achieved results comparable to those of conventional artificial neural networks. In [55], the authors used a fuzzy neural network to predict heart problems based on sound data. This paper proposed a new method for data fuzzification, and the fuzzy network was self-organizing. In [56], the authors introduced a fuzzy neural network for multi-feedback feature selection. The entire solution was applied in power filter systems, enhancing signal filtering capabilities. By integrating carefully designed feedback loops and hidden layers, this approach improves signal evaluation, filtering, and feedback performance. In [57], a vector convolutional neural network was used for fault diagnosis. This solution analyzed the condition of a machine tool and diagnosed faults from signals. Feature extraction utilized convolutional and pooling layers, while classification was conducted using a fuzzy neural network. In [58], the authors utilized a Takagi-Sugeno (T-S) fuzzy neural network for applications in rural logistics. The network consisted of four layers: an input layer, a fuzzification layer, a fuzzy rule computation layer, and an output layer. In [59], the authors developed a probabilistic type-2 fuzzy neural network that is a self-evolving, non-singleton solution. This solution was used for system synchronization and control. In [60], a type-2 fuzzy network was used to control an active power filter. In [61], the authors proposed a wavelet energy fuzzy neural network-based technique for fault protection in microgrids operating in grid-connected mode. This solution required defining fuzzy rules and membership functions. In [62], a fuzzy neural network was utilized for the safe charging of electric vehicles. In [63], the authors used a T-S fuzzy neural network to evaluate water quality. The T-S fuzzy theoretical system is defined using \"if-then\" rules and typically includes four layers: input layer, fuzzification layer, fuzzy rule calculation layer, and output layer. In [64], the authors proposed a recurrent wavelet fuzzy neural network to enhance the performance of a shunt active power filter for harmonic compensation. This filter mitigates reactive power and harmonic current in a power grid. The proposed solution employed a well-known fuzzy network structure with five layers: Layer 1 (input layer), Layer 2 (membership layer), Layer 3 (wavelet layer), Layer 4 (rule layer), and Layer 5 (output layer). In [65], the authors proposed a hybrid approach that integrates deep learning architectures for feature extraction. They used a machine learning classifier combined with a fuzzy min-max neural network. This enabled them to develop a solution for Pap-smear image classification, which is used for cancer detection. The proposed fuzzy classifier consists of three layers: the first for input feature vectors, the second for fuzzy hyperbox sets, and the last for classification nodes. It uses fuzzy membership functions to evaluate how input patterns correspond to different hyperboxes, enabling class label assignment based on membership values.