# Fuzzy Neural Networks--A Review with Case Study
## 2 Fuzzy Neural Network Description
### Papers Review on Defined Criteria
There are solutions in which authors have combined convolutional networks with a fuzzy extraction mechanism using fuzzy analysis, naming the combination a fuzzy convolutional network [9]. Fuzzy networks have been applied within an embedding model for recommendation systems [11]. This approach combines a hierarchical fusion of neural fuzzy and deep network-based embedding architectures. As a result, it enables effective learning of user and item representations while minimizing feature ambiguity and noise. Another approach involves a logical fuzzy neuron based on the concept of null-uninorm [4], known as a null-unineuron, which forms the core of an evolving neuro-fuzzy model. This method allows the neuron to derive complex fuzzy rules that incorporate AND and OR connections of antecedents, improving the interpretability and understanding of the problem being analyzed. However, implementing this solution requires specialized knowledge in fuzzy logic. Fuzzy neural networks have also been utilized multiple times in image processing. In [8], the authors introduced a novel fuzzy metric for quantifying pixel uncertainty. They developed a method where the membership function is used to represent an input image in such a way that the uncertainty of pixels is described by fuzzy rules. The fuzzy layers are then integrated with the convolution results in the neural network. In [23], the authors proposed a solution using a fuzzy neural network in a vehicle braking system that employs an electromechanical actuator. In [24], the authors employed the Takagi-Sugeno-Kang wavelet fuzzy neural network to develop a comprehensive framework dedicated to modeling nonlinear systems by learning from data. Additionally, they utilized Lyapunov stability theory for system optimization. A semi-supervised soft computing approach was proposed in [25] to improve NH3-N prediction accuracy. The authors utilized a fuzzy neural network with a self-constructed architecture and proposed an appropriate learning algorithm, incorporating IF-THEN rules as part of the model. In [26], the authors present the results of a comparison between two solutions: a well-known neural network and a fuzzy network. The conducted tests demonstrate that incorporating a fuzzy layer into the neural network can lead to higher accuracy. In [27] authors used an ANFIS network for the analysis of thermo-acoustic systems. The authors highlighted the advantages of ANFIS:  
* ANFIS is highly effective at modeling complex, nonlinear relationships between inputs and outputs, making it suitable for systems with intricate patterns. It also adapts well to changing environments by dynamically updating its parameters during training to improve performance. They also noted the disadvantages of ANFIS:
* The effectiveness of ANFIS depends heavily on the quality and amount of training data; insufficient or biased data can result in inaccurate models. Additionally, training ANFIS can be computationally intensive, especially with large datasets or complex rule sets, which leads to longer training times and higher resource consumption. In [28], the ANFIS network was used to develop a solution for electricity load forecasting. In [29] such a network was used for data analysis to examine and compare the links between the labor market, demographic factors, and economic performance of developed and less developed European countries. In [30] the same solution was used for clustering American countries affected by the COVID-19 pandemic. In [31], the authors presented a novel version of Support Vector Machines that incorporates an auto-encoder deep neural network consisting of three layers: input, hidden, and output. The encoder functions within the fuzzy domain and employs fuzzy weights, which enhance its robustness against outliers. In [32], a new method was introduced to improve pooling operations in convolutional neural networks by using type-2 fuzzy-based pooling. This technique applies type-2 fuzzy membership functions to better handle local uncertainties in feature maps.