Identificaton-Based Approach to Soft Sensors Design

Bachtadze, Natalya; Kulba, Vladimir; Lototsky, Vladimir A.; Maximov, Evgeny M.

doi:10.3182/20070523-3-es-4908.00015

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…In (Bakhtadze et al, 2007) an approach to form the support of decision making on the control is proposed, based on dynamic modeling the associative search procedure. Results of adoption of the associative search algorithms developed by the authors for industrial processes of the chemical and petroleum manufacturing, processes of control in intelligent power networks (smart grids), trading processes, transport logistic processes.…”

Section: Associative Search Techniquementioning

confidence: 99%

“…Recently, new information technologies and intellectual methods of analysis have become more and more popular (Pavon-Pulido et al, 2017;Biomax, 2018;IBM, 2018). An alternative could be the development of information systems and so-called soft sensors -virtual analyzer to build a predictive model of indicators for the fields of land, based on the intelligent analysis of already available measurement of current and historical data and knowledge base Bakhtadze et al, 2007Bakhtadze et al, , 2012Bakhtadze et al, , 2015.…”

Section: Introductionmentioning

confidence: 99%

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Innovation and Knowledge Management in Precision Farming

Bakhtadze

Maximov

Donchan³

et al. 2019

12th IADIS International Conference Information Systems 2019

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This paper is devoted to the development of information systems for precision farming, which make it possible to use fertilizer and fuel more efficiently on the basis of information technologies and intelligent predictive models, which reduces the cost of production and increases the efficiency of agricultural production. In addition, a long-term agronomic and ecological effect can be achieved due to more careful tillage and reduced use of nitrogen fertilizers. Description of the creating a knowledge base and models of grain yield depending on the mode of mineral fertilizer application based on intelligent associative search algorithms describes.

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Section: Associative Search Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Innovation and Knowledge Management in Precision Farming

Bakhtadze

Maximov

Donchan³

et al. 2019

12th IADIS International Conference Information Systems 2019

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show abstract

“…This may be due to unavailability of hardware sensors, sensors may be costly, available sensors may be with limited measurements, etc. [7,8]. Fig.…”

Section: Introductionmentioning

confidence: 98%

Realization of Deep Learning Based Embedded Soft Sensor for Bioprocess Application

Krishna¹,

Pappa²,

Vasantharani³

2022

Intelligent Automation &Amp; Soft Computing

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“…In the paper, so-called Soft Sensors are proposed [4] built for some indicators for the field pieces by means of predictive models, which are based on intelligent analysis of current and historical data available for measurement.…”

Section: Introductionmentioning

confidence: 99%

Predictive Models for Agricultural Management

Bakhtadze¹,

Maximov²,

Maximova³

et al. 2021

IJESD

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This paper is devoted to the development of predictive models for decision support systems applied in precision farming. Application of predictive models makes it possible to use resources effectively, which reduces the cost of production and increases the efficiency of agricultural production. In addition, the forecast makes it possible to reach a long-term agronomic and ecological effect due to more careful tillage and reduced use of fertilizers. The algorithms using knowledge base for creating models of grain yield are described and the results of applying these models are presented.

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Identificaton-Based Approach to Soft Sensors Design

Cited by 6 publications

References 1 publication

Innovation and Knowledge Management in Precision Farming

Innovation and Knowledge Management in Precision Farming

Realization of Deep Learning Based Embedded Soft Sensor for Bioprocess Application

Predictive Models for Agricultural Management

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