scholarly journals Neural network method of adaptive control system of imitation of physical forces

2020 ◽  
Vol 28 (3) ◽  
pp. 61-74
Author(s):  
Artem D. Obukhov ◽  
Alexandr A. Siukhin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Control Method ◽  
System Delay ◽  
Neural Network Method ◽  
Machine System ◽  
The Neural Network ◽  
Network Method ◽  
Physical Forces

This research examines the subject area of ​​physical forces simulation systems, implemented on the basis of controlled running platforms. The time spent by the control system to receive and process information about the state of the user and the system causes a software and hardware delay that prevents the system from responding in a timely manner to the user's natural movement. The control system delay problem cannot be solved using direct data of the states of the man-machine system. The aim of the presented research is to develop a new control method that allows analyzing the state of the user and the platform, forecasting his movements and organizing the process of managing the system for simulating physical forces. The method is implemented using neural networks. The scientific novelty of the method includes in the use of neural networks to solve the problems of forecasting user actions and automating decision-making to control the system for simulating physical forces. Each presented neural network is formed to perform separate tasks. The first is to create a forecast of changes in the states of a man-machine system. The second is to determine whether the forecasted state belongs to any state in the historical data. The third determines the required change in the states of the parameters of the man-machine system to achieve the forecasted state. The possibilities of using the described approach are presented on the example of a treadmill that adapts to the real parameters of the user's locomotion. The results obtained confirm a significant reduction in the complexity of the implementation of the control process after applying the neural network method. The area of application of the neural network control method is adaptive information systems and automatic control systems, in which it is necessary to minimize the system delay time and response to user locomotion.

scholarly journals Comparative analysis of the geometrized histogram method and the neural network method for road markings recognition

2021 ◽  
pp. 1-22
Author(s):  
Aleksei Valerievich Podoprosvetov ◽  
Dmitry Anatolevich Anokhin ◽  
Konstantin Ivanovich Kiy ◽  
Igor Aleksandrovich Orlov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Computational Complexity ◽  
Video Sequences ◽  
Histogram Method ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Accuracy Speed

This paper compares two approaches to determining road markings from video sequences, namely, the method of finding the markings using geometrized histograms and the method based on neural networks. An independent open dataset TuSimple is used to conduct a comparative analysis of the algorithms. Since the investigated methods have different architectures, their work is evaluated according to the following metrics: Accuracy, speed (relative FPS), general computational complexity of the algorithm (TFlops).

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

ESTABLISHING AN INDEX ARBITRAGE MODEL BY APPLYING NEURAL NETWORKS METHOD — A CASE STUDY OF NIKKEI 225 INDEX

2001 ◽  
Vol 11 (05) ◽  
pp. 489-496
Author(s):  
AN-PIN CHEN ◽  
CHIEH-YOW CHIANGLIN ◽  
HISU-PEI CHUNG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Index ◽  
The Neural Network ◽  
Time Period ◽  
Network Method ◽  
Index Arbitrage ◽  
Impact Risk ◽  
Cost Of Carry

This paper applies the neural network method to establish an index arbitrage model and compares the arbitrage performances to that from traditional cost of carry arbitrage model. From the empirical results of the Nikkei 225 stock index market, following conclusions can be stated: (1) The basis will get enlarged for a time period, more profitability may be obtained from the trend. (2) If the neural network is applied within the index arbitrage model, twofold of return would be obtained than traditional arbitrage model can do. (3) If the T_basis has volatile trend, the neural network arbitrage model will ignore the peak. Although arbitrageur would lose the chance to get profit, they may reduce the market impact risk.

scholarly journals Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes

2019 ◽  
Vol 125 ◽  
pp. 15006
Author(s):  
Taufik Mawardi Sinaga ◽  
M. Syamsu Rosid ◽  
M. Wahdanadi Haidar
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Frequency ◽  
Seismic Attributes ◽  
Seismic Inversion ◽  
Carbonate Reservoir ◽  
Neural Network Method ◽  
Time Migration ◽  
The Neural Network ◽  
Network Method

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

Vibration Control System of Precision Dicing Saw Machine Based on Neural Network Method

2014 ◽  
Vol 577 ◽  
pp. 228-231
Author(s):  
Yan Ma ◽  
Xue Feng Bai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Neural Network Method ◽  
Compensation Methods ◽  
Network Method ◽  
Feedback Method ◽  
Run Time

The precision dicing machine is adopted as the research object in the paper, the technology of grating sensor is selected to overcome the shortcoming of traditional compensation methods for inaccurate return compensate. The measurement is done through the grating sensor, with the aid of neural network feedback method, the fitting and compensation of the data measured by the grating sensor is completed, the deviation caused by vibration during the spindle run-time is eliminated. And the purpose of improving the precision of spindle is realized.

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