Existence and Global Attractivity of Pseudo Almost Periodic Solutions for Clifford-Valued Fuzzy Neural Networks with Proportional Delays

In this paper, Clifford-valued fuzzy neural networks with proportional delays, whose leakage term coefficients are also Clifford numbers, are considered. Based on the Banach fixed point theorem and differential inequality technique, we use a direct method to obtain the existence, uniqueness, and global attractivity of pseudo almost periodic solutions for the considered networks. Finally, we provide a numerical example to illustrate the feasibility of our results. Our results are new.

Assessment automation of road traffic safety with account for road conditions of an individual itinerary

The paper substantiates the need to develop an automated system for traffic safety assessment in urban agglomerations, taking into account road conditions. The authors suggest a methodology for assessment of road traffic accidents, which makes it possible to take into account a wide range of factors affecting them. The methodology is based on complementing the traditional approach of final accident rate calculation with algorithms for collecting and analyzing data using Big Data tools, in particular, convolutional neural networks, fuzzy neural networks such as ANFIS, and cluster analysis using the k-means method. All accident rates are grouped according to the principle of homogeneity of acquisition of information for their calculation. Further, one of the data processing tools is applied to each group. As a result, labor intensity is reduced and the effectiveness of the application of the method of final accident rates increases. For practical calculations, the authors have developed a client-server application that uses data on geometric characteristics, current traffic situation, weather and climatic effects at the time of the trip along a specific itinerary. By means of application use, the analysis of traffic safety on a number of routes in Volgograd was carried out and the results are presented in comparison with the calculations made via the traditional method. It is shown that the use of information about the current situation on a specific section of the road network in terms of the current time significantly increases the accuracy of calculations.

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.