scholarly journals INTERNET USER’S BEHAVIOR FROM THE STANDPOINT OF THE NEURAL NETWORK THEORY OF SOCIETY: PREREQUISITES FOR THE META-EDUCATION CONCEPT FORMATION

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 83-90
Author(s):  
A. S. Bakirov ◽  
Y. S. Vitulyova ◽  
A. A. Zotkin ◽  
I. E. Suleimenov
Keyword(s):  
Neural Network ◽  
Information Sources ◽  
Point Of View ◽  
Collective Unconscious ◽  
Information Structures ◽  
Dual Nature ◽  
Internet Users ◽  
The Neural Network ◽  
Meta Learning ◽  
Neural Network Theory

Abstract. An analysis of the behavior of Internet users from the point of view of their preferences in the choice of information sources and the effectiveness of their impact is presented. It is shown that the modern infocommunication space has undergone qualitative changes in the most recent time, and these transformations are already having a pronounced impact on higher education, mainly through the factor of competition between information sources. It is shown that these transformations can be interpreted as the evolution of the noosphere, which is considered as a global infocommunication network, in which non-trivial transpersonal information objects are formed. Their existence leads to the fact that the human intellect has a dual nature - both individual and collective principles are present in it at the same time. The latter is responsible for such phenomena as the collective unconscious, understood in the sense of Jung. It is shown that the neural network model of the noosphere makes it possible to formulate a similar concept of "professional collective unconscious", which is responsible for professional intuition, acts of creativity, etc. In turn, the existence of the professional collective unconscious forces us to radically reconsider the content of what is called training and move to the concept of meta-learning, which, among other things, involves stimulating transitions from one level of interaction with transpersonal information structures that make up the professional collective unconscious to another.

ANALYSIS OF HISTORICAL DATA USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR PREDICTING THREATS IN COMPLEX DISTRIBUTED SYSTEMS

2021 ◽  
pp. 39-45
Author(s):  
E. V. Palchevsky ◽  
O. I. Khristodulo ◽  
S. V. Pavlov ◽  
A. V. Sokolova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distributed Systems ◽  
Historical Data ◽  
Mathematical Formulation ◽  
Prediction Method ◽  
Point Of View ◽  
The Neural Network ◽  
Water Rise ◽  
Artificial Neural

A threat prediction method based on the mining of historical data in complex distributed systems is proposed. The relevance of the selected research topic is substantiated from the point of view of considering floods as a physical process of water rise, the level of which is measured at stationary hydrological posts. The mathematical formulation of the problem is formulated, within the framework of which an artificial neural network is implemented based on the free software library “TensorFlow”. An analysis of the effectiveness of the implemented artificial neural network was carried out, according to the results of which the weighted mean square-law deviation of the predicted water level value from the actual one when forecasting for one day at stationary hydrological posts was 0.032. Thus, the neural network allows predicting the flood situation with acceptable accuracy, which gives time for special services to carry out measures to counter this threat.

scholarly journals Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Antonino Laudani ◽  
Gabriele Maria Lozito ◽  
Francesco Riganti Fulginei ◽  
Alessandro Salvini
Keyword(s):  
Neural Network ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Point Of View ◽  
Reduced Form ◽  
Network Approach ◽  
Neural Network Approach ◽  
Hybrid Neural Network ◽  
The Neural Network ◽  
Two Parameters

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

scholarly journals Estimating Gas Turbine Internal Cycle Parameters Using a Neural Network

1996 ◽  
Author(s):  
Nicolas W. Chbat ◽  
Ravi Rajamani ◽  
Todd A. Ashley
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Back Propagation ◽  
Point Of View ◽  
Digital Controller ◽  
Reference Temperature ◽  
Current Estimate ◽  
Critical Mode ◽  
The Neural Network ◽  
Internal Parameters

We show that a neural network can be successfully used in place of an actual model to estimate key unmeasured parameters in a gas turbine. As an example we study the combustion reference temperature, a parameter that is currently estimated via a nonlinear model inside the controller and is used in a number of critical mode-setting functions within the controller such as calculating the fuel-split between various manifolds. We show that a feedforward neural network using simple back propagation learning can be built for estimating combustion reference temperature. The neural network matches the accuracy of the current estimate; and it is more robust to errors in its internal parameters. This is advantageous from the point of view of implementation since a number of errors creep in when running the algorithm on a digital controller, and an estimator that is not robust with respect to such errors can degrade the performance of the whole system.

Simulation of Failure Detection Based on Neural Network for No-Ball Mill

2011 ◽  
Vol 201-203 ◽  
pp. 627-631
Author(s):  
Kun Shan Li ◽  
Xin Hua Wang ◽  
Wen Ming Wang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Network Theory ◽  
Ball Mill ◽  
Detection System ◽  
Structural Characteristics ◽  
Failure Detection ◽  
The Neural Network ◽  
Point Set ◽  
Neural Network Theory

According to the structural characteristics of non-ball mill, using the neural network theory to select and measure point, set the failure mode, analyze and determine the cause of malfunction. The newly developed fault detection system was used to simulative detect fault. Through data processing, the results can be directly derived which could be fed back into the design of non-ball mill, thereby improving the design.

The Establishment of Land Appraisal Model in Xiamen Based on BP Neural Network

2012 ◽  
Vol 518-523 ◽  
pp. 6084-6087
Author(s):  
Qing Ye ◽  
Ya Yi Su ◽  
Fei Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Theory ◽  
Evaluation Model ◽  
Land Evaluation ◽  
Bp Network ◽  
The Neural Network ◽  
Hidden Layer ◽  
Neural Network Theory ◽  
Error Index

Establish the land evaluation model of Xiamen by means of BP neural network theory, taking 2007-2009 land evaluation cases of Xiamen as examples. Through statistical analysis, we find that the neural network which has 9 net work hidden layer nodes and 19% of maximal error index is more suitable for Xiamen land price assessment than others. Empirical analysis shows that the model has a good generalization ability, which can be used for land evaluation practices. The results indicates that the properties of autonomous learning of BP network can reduce the subjective factors of appraiser in land evaluation , also, the network has the advantage of simple and quick calculation.

Time Series Analysis of Slope Displacement Using Neural Network Method

2013 ◽  
Vol 405-408 ◽  
pp. 129-132
Author(s):  
Zhi Qiang Zhang ◽  
Yan Liang Wen ◽  
Guo Jian Zhang ◽  
Lai Shan Chang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Theory ◽  
System Analysis ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Network Method ◽  
Neural Network System ◽  
Neural Network Theory ◽  
Slope Displacement

Based on the artificial neural network theory, a neural network approach is proposed for the analysis of slope displacement time series, the neural network system analysis of slope displacement time series is developed, it is proved that this method is scientific and reasonable.

The Pattern Recognition Method for Wear Particle Image Based on Rough Sets — Neural Network Theory

2014 ◽  
Vol 1037 ◽  
pp. 345-348
Author(s):  
Shi Hong Bai ◽  
Li Rong Guan ◽  
Yan Jing Wang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Rough Set ◽  
Wear Particle ◽  
Recognition Method ◽  
The Neural Network ◽  
Neural Network System ◽  
Knowledge Representation System ◽  
The Difference ◽  
Neural Network Theory

By analysis the difference of applying the rough set method and the neural network method to pattern recognition, a improved recognition method that the rough set method is the front system of neural network was produced. the advantages of this method is that the knowledge representation system is reduced without affecting the recognition precision, so the complexity of neural network system and the time of calculating the attribute value is declined ; at the same time ,the neural network as the postpositional system has the tolerance and anti-jamming capability, but it is difficult to do this with rough set method. The example about how to combine these two methods and conclusions from this combination was given.

Sign in / Sign up

Export Citation Format

Share Document