# Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies
## 2 Current Fuzzy Neural Networks Review  
A lot of research dealing with fuzzy neural networks has been conducted. Some use the McCulloch-Pitts model of a neuron and change the activation function to the fuzzy function, which makes the neuron more general [1]. Some authors [2] change the neuron architecture to use triangular fuzzy weights. This approach uses fuzzy vectors and real numbers as inputs. The outputs of such networks are fuzzy vectors. Such solutions are possible by using the extension principle of Zadeh. Most papers use fuzzy weights and signals in the network [3]. [PERSON] et al. [4] propose a fuzzy neuron using the idea of null-uninorm, called null-unineuron. It allows building an evolving neuro-fuzzy model. It leads to the extraction of advanced fuzzy rules and/or connections of antecedents. The authors used a three-layer model with a weighted fuzzification approach based on incremental data partitioning concepts for knowledge extraction. The main use of fuzzy networks is to build controllers that will work better than ordinary ones [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. There are some papers in which the authors describe how to use a fuzzy network algorithm for labeling images [17]. [PERSON] et al. [18] provide information on how to use an unsupervised multilayer fuzzy neural network, which could be used for image clustering. The authors extend fuzzy systems to unsupervised tasks by proposing manifold representation. There are also some works on using fuzzy neural networks for medical image recognition. The authors defined the problem that medical images can often blur object boundaries, and it is impossible to describe the uncertainty of these blurred boundaries using traditional neural network models. They propose a new fuzzy metric to characterize the uncertainty of pixels and design a fuzzy hierarchical fusion attention neural network based on multiscale guided learning. The authors also proposed data transformation to convert the image into a fuzzy domain, which allows using fuzzy rules to deal with the uncertainty of the pixels. Then, the results are connected with the result of the convolution in the neural network [19]. [PERSON] and [PERSON] proposed a combined method for bankruptcy prediction based on fuzzy set qualitative comparative analysis and convolutional neural networks [20]. Some authors propose using an adaptive neural network for solving some real problems, e.g., the problem of the Takagi-Sugeno fuzzy model-based state estimation and sliding mode control for nonlinear systems through an adaptive neural network. In this proposition, the system parameters follow the Markovian switching rules [21]. One of the reasons for using a fuzzy theorem with a neural network is to learn the neural network faster [22]. [PERSON] et al. [23] present a fixed-time synchronization for fuzzy inertial neural networks with time-varying coefficients and time delays. The authors propose their criteria to ensure fixed-time synchronization for the drive-response fuzzy inertial neural networks via state feedback control methods. [PERSON] et al. propose a delay-independent nonlinear fuzzy control for solving the synchronization problem for T-S fuzzy memristive neural networks with time delay [24]. In [25], a novel adaptive fuzzy neural network-aided progressive Gaussian filter is proposed to solve the problem of uncertainty of prior information in nonlinear filtering, which could be used in navigation and positioning systems. In [26], a deep fuzzy neural network is used for controlling the integrity of the reactor core, reactor coolant system, or containment. In [27], the authors propose fuzzy reinforced polynomial neural networks. The results provided show that the proposed architecture achieves at most an accuracy 43.6% higher in comparison with the accuracy produced by some classical models reported in the literature. In [28], the authors propose an architecture of a fuzzy neural network based on self-organizing direction-aware data using stochastic processes.