Add to ORKGln this paper the features of neural networks using for improve of measurement accuracy of physical quantities by sensor drift prediction are considered. There is use a technique of data volume increasing for training of predicting neural network by using of separate approximating neural network
AbstractThe article describes three approaches in error correction of dynamic measurements. A sensor...
Abstract This article gives a brief description of the main methods of forming parallel ensembles of...
This research project develops a new deep neural network model for real-time human movement predicti...
Many industrial, medical, automotive, and consumer applications increasingly demand highly accurate ...
In this paper, artifi cial neural networks (ANNs) were used to assess the performance of fl ow meter...
In this paper, a new mSom neural network methodology has been developed and applied to improve the c...
This paper work refers to the prediction problems which are used with the help of the neuronal netwo...
Due to the lack in selectivity and separability of most common chemical sensors, the use of sensor a...
Accurate measurement of temperature, pressure and volume in a gas metering stations is an important...
The paper presents a technology based on an ensemble of neural networks that solves the problem of p...
A strategy has been developed to computationally accelerate the response time of a generic electroni...
Significant technical development over the last years has lately been showing more and more promise ...
The applied chemical sensor research focuses on sensor arrays and signal evaluation methods, to impr...
Sloshing causes liquid to fluctuate, making accurate level readings difficult to obtain in dynamic e...
Sensor drift is a phenomenon which indicates unexpected variations in the sensory signal responses b...
AbstractThe article describes three approaches in error correction of dynamic measurements. A sensor...
Abstract This article gives a brief description of the main methods of forming parallel ensembles of...
This research project develops a new deep neural network model for real-time human movement predicti...
Many industrial, medical, automotive, and consumer applications increasingly demand highly accurate ...
In this paper, artifi cial neural networks (ANNs) were used to assess the performance of fl ow meter...
In this paper, a new mSom neural network methodology has been developed and applied to improve the c...
This paper work refers to the prediction problems which are used with the help of the neuronal netwo...
Due to the lack in selectivity and separability of most common chemical sensors, the use of sensor a...
Accurate measurement of temperature, pressure and volume in a gas metering stations is an important...
The paper presents a technology based on an ensemble of neural networks that solves the problem of p...
A strategy has been developed to computationally accelerate the response time of a generic electroni...
Significant technical development over the last years has lately been showing more and more promise ...
The applied chemical sensor research focuses on sensor arrays and signal evaluation methods, to impr...
Sloshing causes liquid to fluctuate, making accurate level readings difficult to obtain in dynamic e...
Sensor drift is a phenomenon which indicates unexpected variations in the sensory signal responses b...
AbstractThe article describes three approaches in error correction of dynamic measurements. A sensor...
Abstract This article gives a brief description of the main methods of forming parallel ensembles of...
This research project develops a new deep neural network model for real-time human movement predicti...