Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies

Abstract

The general goal of the research in this article is to devise an artificial neural network that requires less computational power than an ordinary one for assessing overall life satisfaction—a term often referred to as quality of life (QoL). The development of the mentioned ANN was possible due to the application of fuzzy logic, especially ordered fuzzy numbers (OFN). Research on the appliance of OFN aims at different issues such as the detection of an attack on a computer network, the anticipation of server load, management of multiplexing of data transmission paths, or transmission error rate forecasting that allows the improvement of the quality of life. It occurs due to, for instance, reduced energy demand, savings through better data transmission, and the distribution of computers’ power used in the cloud. Finally, the application of OFN on single neurons of a deep ANN allows achieving a network that is able to solve the same problem as a normal network, but with a lower number of neurons. Such networks in the future may be implemented easier in small solutions, such as solutions for the Internet of Things to improve the quality of human life. This approach is unique and has no equivalent in the literature. Due to the application of OFN in an ANN, fewer requirements for network architecture were needed to solve the same problems, and as a result, there is less demand for processor power and RAM.

Taking Flight for a Greener Planet: How Swarming Could Help Monitor Air Pollution Sources

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