This work proposes a novel approach to Soft Sensor modelling, where the Soft Sensor is built by a set of experts which are artificial neural networks with randomly generated topology. For each of the experts a meta neural network is trained, the gating Artificial Neural Network. The role of the gating network is to learn the performance of the experts in dependency on the input data samples. The final prediction of the Soft Sensor is a weighted sum of the individual experts predictions. The proposed meta-learning method is evaluated on two different process industry data sets
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditi...
With the predicted depletion of natural resources and alarming environmental issues, sustainable dev...
Soft sensors with real time prediction capabilities appear as a profitable solution for hard-to-meas...
This paper gives a general overview of the challenges present in the research field of Soft Sensor ...
Soft sensors are a gradually expanding technique in the field of industrial measurement. These senso...
In process industries, there is a great demand for additional process information such as the produc...
In process industries, there is a great demand for additional process information such as the produc...
The lack of real-time measurement of certain critical product and process characteristics is a major...
Neural network-based soft sensors are developed for kerosene properties estimation, a refinery crude...
Automatic data acquisition systems provide large amounts of streaming data generated by physical sen...
Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of ...
Chemical use advanced automation systems that provide large amounts of accurate data information fro...
Neural network-based soft sensors are developed for kerosene properties estimation, a refinery crude...
Soft sensors have been widely used in the industrial process control to improve the quality of the p...
The enormous technological growth increases the application of machine learning in the petrochemical...
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditi...
With the predicted depletion of natural resources and alarming environmental issues, sustainable dev...
Soft sensors with real time prediction capabilities appear as a profitable solution for hard-to-meas...
This paper gives a general overview of the challenges present in the research field of Soft Sensor ...
Soft sensors are a gradually expanding technique in the field of industrial measurement. These senso...
In process industries, there is a great demand for additional process information such as the produc...
In process industries, there is a great demand for additional process information such as the produc...
The lack of real-time measurement of certain critical product and process characteristics is a major...
Neural network-based soft sensors are developed for kerosene properties estimation, a refinery crude...
Automatic data acquisition systems provide large amounts of streaming data generated by physical sen...
Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of ...
Chemical use advanced automation systems that provide large amounts of accurate data information fro...
Neural network-based soft sensors are developed for kerosene properties estimation, a refinery crude...
Soft sensors have been widely used in the industrial process control to improve the quality of the p...
The enormous technological growth increases the application of machine learning in the petrochemical...
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditi...
With the predicted depletion of natural resources and alarming environmental issues, sustainable dev...
Soft sensors with real time prediction capabilities appear as a profitable solution for hard-to-meas...