State Observer based Elman Recurrent Neural Network for Electric Drive of Optical-Mechanical Complexes

2021 ◽  
Author(s):  
Mikhail P. Belov ◽  
Nguyen Van Lanh ◽  
Tran Dang Khoa
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Electric Drive ◽  
State Observer ◽  
Elman Recurrent Neural Network

scholarly journals ПІДХОДИ ДО СТВОРЕННЯ ПРОГРАМНОГО АГЕНТА НА ОСНОВІ МЕТАЕВРИСТИК І ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ

2019 ◽  
pp. 58-65
Author(s):  
Євген Євгенович Федоров ◽  
Марина Володимирівна Чичужко ◽  
Владислав Олегович Чичужко
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Internal State ◽  
Numerical Study ◽  
Agent Based ◽  
Development Framework ◽  
Number Of Visits ◽  
Artificial Neural ◽  
The Creation ◽  
Elman Recurrent Neural Network

In this article, has been developed a software agent based on meta-heuristics and artificial neural networks. The analysis of existing classes of agents and the selected reactive agent with internal state, which is well suited for partially observable, dynamic and non-episodic media, was carried out, and this agent has an internal state that preserves the state of the environment, obtained on the basis of the history of acts of perception, in the form of structured data. Were proposed approaches to create an agent based on meta-heuristics and an agent based on an artificial neural network. The development of reactive agents with internal state, based on the PSO (particle swarm optimization) metaheuristics, which are related to individual particles and to a whole swarm and interact by messages was proposed. Also, has been proposed an approach to the creation of a reactive agent with an internal state based on the Elman recurrent neural network. The agent-based approach allows combining different areas of artificial intelligence, digital signal processing, mathematical modeling, and game theory. The proposed agents were implemented using the JADE (Java Agent Development Framework) toolkit, which is one of the most popular tools for the creation of agent systems. A numerical study was made to determine the parameters of the swarm PSO metaheuristics and the Elman recurrent neural network. As a purpose function, the Rastrigin test function has been used. The number of visits to the website of DonNTU was used as an input sample for the Elman network. The minimum average square error forecast was the criterion for choosing the structure of a network model. 10 hiding neurons were used to predict the number of visits to the website page, since, with increasing of hidden neurons number, the change in the error value is small. To determine the number of particles in the swarm, a series of experiments was conducted, the results of which are presented by graphs. The proposed approaches can be used in intelligent computer systems.

scholarly journals Classification of Eog Signal using Elman Recurrent Neural Network for Different Age Groups

2019 ◽  
Vol 9 (2S2) ◽  
pp. 842-847
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Age Groups ◽  
Computer Interface ◽  
World Population ◽  
Age Group ◽  
The Individual ◽  
Elman Recurrent Neural Network ◽  
Male Subjects

Disabled people in the world population were increasing constantly, So need of rehabilitative system also increasing every day. To overcome such wretched condition, we can use the biosignal techniques to device the rehabilitative devices. Rehabilitative devices may be called as Brain Computer Interface (BCI) or Human Computer Interface (HCI). We studied the performances of ten male subjects between the age group of 18 to 25 using mean features and Elman Recurrent Neural Network (ERNN). We conducted our study with two different age group from 18 to 21 and 22 to 25. The average classification accuracy of 91.00%, 93.57% were attained for the age group of 18 to 21 and 22 to 25. From the individual analysis we identified that performances from the age group 22 to 25 were appreciated then that of the age group from 18 to 21. In between the study we analyzed that subject s from the age group 22 to 25 performed all the following five tasks neatly and accurately without any deviation and disturbance compared with age group from 18 to 21. Finally from the obtained result we concluded that subject from the age group 22 to 25 was higher than that of age group from 18 to 21.

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