# Fuzzy Neural Networks--A Review with Case Study
## 2 Fuzzy Neural Network Description
### Papers Review on Defined Criteria  
Some propose the use of the McCuloch-Pitts neuron when extended to a fuzzy model with activation calculation in the fuzzy logic domain [1]. Some authors propose the use of fuzzy number representation in the triangular form [2]. Such network solutions allow both fuzzy and real vectors as input. However, in both cases, the network outputs are fuzzy outputs. The relation between the inputs and outputs is defined as a Zadeh extension. Manypublications use fuzzy signals and weights [3]. [PERSON] presented a new logic of the fuzzy neuron in his publication [4]. In the applied structure, the neuron can perform fuzzy calculations involving logical AND and OR operations, which, according to the author of the publication, allow better interpretation and understanding of the problem. The proposed three-layer model uses a fuzzy approach with weight assignment allowing knowledge extraction in an iterative manner. One of the broad applications of fuzzy networks is the construction of fuzzy controllers, which, in principle, are supposed to perform better than traditional solutions [2; 5]. Fuzzy logic algorithms are also used, for example, for labeling large sets of images [6]. One can also find proposals for unsupervised multilayer neural network solutions for image clustering using fuzzy system and convolutional networks [7]. Fuzzy networks have also already been used to recognize medical images. These images often have blurred object boundaries; in such a case, traditional methods have difficulties in recognizing the objects. Fuzzy logic comes in here as assistance, where due to a fuzzy processing block, the image is converted by assigning individual pixels the value of a fuzzy set membership function. After image processing in the convolutional layer of the neural network [8], the fuzzy rules are used. A combination of fuzzy logic and neural networks has also been used to predict bankruptcy [9]. Some authors propose the use of adaptive neural networks to solve the state estimation problem using the fuzzy Takagi-Sugeno model and the control of nonlinear systems through an adaptive neural network in which the system parameters comply with Markov switching rules [10]. One of the advantages of using fuzzy logic in neural networks is that it accelerates the learning process of the network [11]. Fuzzy neural networks have also been used to synchronize the feedback controller [12; 13], where the authors proposed several innovative criteria. Fuzzy logic has also been used for the signal filtering process, using an adaptive fuzzy, progressive Gaussian filter supported by neural networks in navigation and positioning systems [14]. Deep fuzzy neural networks have also been used to control the integrity of the reactor core, reactor cooling system, or containment [15]. The application of fuzzy logic in reinforcement learning allowed a gain in accuracy 43.6% higher in comparison with the accuracy achieved by some classic models described in the literature [16]. An increase in the network accuracy following the implementation of fuzzy logic has also been reported in self-organizing networks [17]. One can also find proposals of fuzzy recurrence neural networks, e.g., consisting of two fuzzy neural networks with Takagi-Sugeno-Kang rules. One is used to generate a fuzzy result and the other to determine the state of the system while maintaining the coupling between these networks [18]. Proposing new fuzzy networks generates the problem of developing learning algorithms, often hybrid algorithms developed from existing ones [19]. Learning algorithms have already been proposed that are not sensitive to the initial values of the weights and do not require high computing power, which is very important for the application of solutions in, for example, the Internet of Things [20]. In fuzzy networks, it is necessary to develop activation functions (e.g., sigmoidal) [21; 22].