THE AUTOMATIC SYNTHESIS OF PETRI NETS BASED ON THE FUNCTIONING OF ARTIFICIAL NEURAL NETWORK

Context. The important task was solved during the scientific research related to the development of the methods for automatic synthesis of Petri nets while tuning up of the coordinating automatic control systems. The importance of development of these methods is due to the evolution of intelligent systems. These systems provide the automation of labor intensive processes in the particular case this is the tuning of the certain type of complex control systems. Objective. The purpose of the scientific work is to minimize the time and automation of process in tuning of the multilevel coordinating automatic control systems. Method. The principle of automatic synthesis of Petri nets and the implementation of certain algorithms for tuning complex control systems based on the functioning of an artificial neural network are proposed. The mathematical description of the method for changing the coefficients in neural connections of network in the synthesis of Petri nets is presented. Results. The experiments were conducted in the Matlab\Simulink 2012a environment. These experiments were bound to the joint functioning of an artificial neural network and Petri nets. The functioning of Petri nets was presented in the Matlab \ Simulink environment using Statflow diagrams. As a result of the experiments we have obtained the temporal characteristics of the functioning of artificial neural network providing the composition of Petri nets. The fundamental suitability of using artificial neural network to provide the automatic composition of Petri nets was determined on the basis of analysis of temporal characteristics. Conclusion. The problem linked to the development of system for the joint functioning of neural network and Petri nets for the formation of algorithms and sequential calculations was solved in this work. Thus the method of automatic synthesis of Petri nets and the method of developing of the certain algorithms based on the functioning of a neural network were further developed.

Download Full-text

AUTOMATIC SYNTHESIS OF PETRI NETS AT TUNING UP OF THE COORDINATING AUTOMATIC CONTROL SYSTEMS

ELECTRICAL AND COMPUTER SYSTEMS ◽

10.15276/eltecs.32.108.2020.4 ◽

2020 ◽

Vol 33 (108) ◽

pp. 34-44

Author(s):

A. A. Gurskiy ◽

◽

A. E. Goncharenko ◽

S. M. Dubna

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Control System ◽

Automatic Control ◽

Petri Nets ◽

Automatic Control System ◽

Automatic Synthesis ◽

Coordinating Control ◽

Artificial Neural ◽

Automated Tuning

The process of automated tuning for the coordinating automatic control system is considered in this paper. This process of tuning for the coordinating control system is linked to the automatic synthesis of Petri nets based on functioning of the artificial neural network. Thereby, we can automate the process of tuning and synthesis of system models and also solve the urgent task linked to the minimization of tuning time for the multilevel control systems. The purposes of the scientific work are time reduction of the tuning and automatization of the tuning for the multilevel coordinating systems of the automatic control. In order to achieve this purpose in the MATLAB \ Simulink software environment it is necessary to devel- op the system for automated tuning of the regulators of various levels for the coordinating automatic control system. The application of artificial neural network with automatic synthesis of Petri nets allows to introduce intelligent technology in the automated tuning system. In this work we have presented the corresponding block diagrams of considered automated tuning system and the principles of its functioning. The certain principle of the formation of Petri nets is proposed. These Petri nets represent the algorithms of tuning in the systems for analysis the corresponding processes. The formation of the composition in the scheme from Petri net during the functioning of the artificial neural network is presented in the paper. The results of experiment are presented in the final part of this work. This time characteristics of the pro- cess of setting up for the coordinating automatic control system of foodstuffs cooling in tunnel chamber. The experiments were conducted in the Matlab 2012a environment. Based on the results of the experiment we have depicted the process of synthesis of the Petri net representing the system tuning algorithm. The performed experiments have showed the principal suitability of the automated search system for the settings of the regulators of various levels of the coordinating control system. The technique of automatic synthesis of Petri nets based on the functioning of artificial neural networks has obtained the further devel- opment while performing the approved task in the scientific paper.

Download Full-text

DEVELOPMENT OF THE AUTOMATIC SYSTEM OF SHEET HIRE PREPARATIONS POSITIONING

IZVESTIA VOLGOGRAD STATE TECHNICAL UNIVERSITY ◽

10.35211/1990-5297-2020-8-243-72-74 ◽

2020 ◽

pp. 72-74

Author(s):

S. P. Smolyakov ◽

V. D. Zhokhov ◽

V. G. Barabanov

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Control System ◽

Automatic Control ◽

Control Systems ◽

Automatic Control System ◽

Automatic System ◽

Positioning Systems ◽

Positioning Device ◽

Artificial Neural

The paper analyzes the existing positioning systems of metal billets and their control systems. An automatic control system for a positioning device based on an artificial neural network has been developed.

Download Full-text

Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network

Security and Communication Networks ◽

10.1155/2021/4027900 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Chao Wang ◽

Bailing Wang ◽

Yunxiao Sun ◽

Yuliang Wei ◽

Kai Wang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intrusion Detection ◽

Control Systems ◽

Industrial Control ◽

Industrial Control Systems ◽

Open Set ◽

The Neural Network ◽

Center Loss ◽

Artificial Neural

The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.

Download Full-text