Open AccessEMERGENCY CONTROL SYSTEM BASED ON NEURAL NETWORKS AND FUZZY LOGIC
Author(s) -
Sergii Konovalov,
G.A. Yegoshyna,
S.M. Voronoy
Publication year - 2020
Publication title -
naukovì pracì onaz ìm. o.s. popova
Language(s) - English
Resource type - Journals
eISSN - 2518-7147
pISSN - 2518-7139
DOI - 10.33243/2518-7139-2020-1-1-45-52
Subject(s) - artificial neural network , fuzzy logic , computer science , knowledge base , reliability (semiconductor) , expert system , neuro fuzzy , hierarchy , artificial intelligence , set (abstract data type) , reliability engineering , fuzzy control system , machine learning , data mining , engineering , power (physics) , physics , quantum mechanics , economics , market economy , programming language
The presented paper investigates the problem of ensuring the safety of modern vessels, represented as complex organizational and technical systems. This study solves the task of diagnosing and predicting the level of ships’ operational reliability using a hybrid expert system based on a combination of a neural network and fuzzy logic. Trends in modern control systems show that they must be adaptive and intelligent. However, these requirements cannot be met by expert systems based only on fuzzy logic. This work explores the possibility of combining neural network modules with fuzzy logic and considers the features of emergency management stages based on the offered hybrid expert system. The input information arrives in a knowledge base through gauges, where it is structured and distributed in the form of performance indicators. Emergency recommendations for the operator are formed as a result of a combination of performance indicators available in the knowledge base. Modules of the neural network and fuzzy logic form a system for assessing a complex technical system’s health based on calculated estimates of the health of technical nodes. In addition, the authors formed a hierarchy of factors affecting the reliability of the system. While developing the knowledge base, critical values for each variable influencing the system performance are set, and when the values are reached, the operation mode becomes an emergency. The authors chose a multilayer perceptron with a layer of recurrent neurons and inputs as fed factors and criteria for performance; one output displays the value of system performance. Prediction of the technical state of the system is made based on time series analysis. The system with six variables was used as a test set, three of which are non-linguistic (efficiency coefficient, temperature, and pressure). The standard linguistic variable, calculated by the neural network, includes speed, fuel consumption, and wear of the node. The fuzzy logic module was used to form recommendations for the prevention or elimination of an emergency.