The work is devoted to building fast artificial neural networks based on a relatively new activation function called “s-parabola” and committees (combinations) of similar networks. The relevance of the study is determined by the need for operational processing of images delivered by unmanned aerial vehicle machine vision systems in order to detect smoke and fires in forest areas. The proposed solution is an alternative to building convolutional neural networks with typical activation functions. The most important application of such networks is the early recognition of forest fires in real time. An experimental study of the quality of multilayer neural networks with the function of activating s-parabola and their committees was performed using various sets of informative features and a generalized metric. The presence of accelerated training of networks with this activation function at high recognition accuracy indicators determines the feasibility of applying the proposed approach in the tasks of intelligent support for operators of visual analysis of fast processes.
Math-Net.Ru: https://www.mathnet.ru/eng/iipr625
M. V. Khachumov, Yu. G. Emel'yanova, V. P. Fralenko. Fast artificial neural networks and their application as a part of classifier committees for operational fire detection // Artificial Intelligence and Decision Making, 2025, Issue 2, pp. 32–41.