Inflation expectations quantification with fuzzy control system

The inflation expectations of economic agents are one of the most important variables for central banks and monetary policy conduct, but such expectations are not directly observable. Consumers’ expectations are examined in qualitative surveys. The key task is to transform consumers’ survey responses into quantitative proxies of expectations. In this examination, we investigate an alternative method to quantify consumers’ inflation expectations using fuzzy rule-based systems. In order to generate rules in the learning phase, we compare two methods: the Wang-Mendel method based on space partition and the subtractive clustering method. The learning data are information about past inflation and consumers’ opinions as expressed in surveys. The system is built and tested on data from January 2002 to June 2019 for non-euro member states of the EU. The fuzzy rules-based system returns results that outperform a benchmark—the standard quantification procedure—in terms of correlation with a raw approximation of consumer expectations, which is survey balance statistics. The results are robust regarding the benchmark choice. We find the method more promising for economies with lower stability, as it gives more room for the training phase of the algorithm application.

Algorithms for Creating the Digital Twin of a Distributed Generator

The article describes the concept of a digital twin of a distributed generator (DG) implemented on the basis of a salient-pole synchronous generator. The structure of the digital twin is presented in the form of a hierarchical fuzzy model built on the experimental data. For building fuzzy models of individual elements and links of a generator, software algorithms for optimization of the membership functions of term sets and the numbers of knowledge base rules are proposed. The results of experiments on obtaining an optimized neuro-fuzzy model for regulating the rotation speed of the generator rotor are presented. A comparative analysis of the oscillograms of the output signal of the optimized fuzzy model showed a sufficiently high accuracy when using the subtractive clustering method to build a fuzzy logic system. This approach made it possible to significantly reduce the number of term sets and the amount of the fuzzy model rules. The proposed algorithms can be used in further research aimed at industrial implementation of DG digital twins, as well as for solving the problem of tuning automatic regulators of low-power synchronous generators

An energy-efficient clustering protocol using fuzzy logic and network segmentation for heterogeneous WSN

Wireless sensor networks have become an emerging research area due to their importance in the present industrial application. The enlargement of network lifetime is the major limitation in WSN. Several routing protocols study the extension of lifespan in WSN. Routing protocols significantly influence on the global of energy consumption for sensors in WSN. It is essential to correct the energy efficiency performance of routing protocol in order to improve the lifetime. The protocols based on clustering are the most routing protocols in WSN to reduce energy consumption. The protocols dedicate to WSN have demonstrated their limitation in expanding the lifetime of the network. In this paper, we present Hybrid SEP protocol : Multi-zonal Fuzzy logic heterogeneous Clustering based on Stable Election Protocol (FMZ-SEP). The FMZ-SEP characterizes by four parameters: WSN segmentation (splitting the WSN into the triangle zones ), the Subtractive Clustering Method to determine a correct number of clusters, the FCM and the SEP protocol. The FMZ-SEP prolong the stability period and extend the lifetime. The simulation results point out that the stability period of FMZ-SEP. FMZ-SEP protocol outperforms of MZ-SEP, FSEP and SEP protocol by improving the network lifetime and the stability period.