A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes

Díaz, Ignacio; Domí­nguez, Manuel; Cuadrado, Abel A.; Fuertes, Juan J.

doi:10.1016/j.eswa.2007.05.031

Cited by 39 publications

(24 citation statements)

References 22 publications

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“…During the learning period the SOM folds itself to fit input data by updating neurons weigh vectors which represents neurons position in the input data space, so the neurons groups are made in areas with high input data density. [11], [12] The competitive learning algorithm in each step compute the distances between all neurons weight vectors and current input from the input data set. The nearest neuron is called BMU -the best matching unit.…”

Section: Middle-scale Boiler Optimization Algorithmmentioning

confidence: 99%

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Advanced control with economic - ecological optimization for biomass-fired boilers

Oswald

Plaček

Šulc

2014

Proceedings of the 2014 15th International Carpathian Control Conference (ICCC)

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This paper presents work progress of advanced control development achieved on a prototype 100kW biomassfired boiler. The aim of the research was to develop a complex control system, which provides both the basic boiler heating performance control based on the actual consumption and the optimization of operating conditions using advanced control algorithms. Thus, the resultant control system should ensure these main control tasks, i.e. to provide the desired boiler power achieved with the minimal fuel consumption and simultaneously minimal operation environmental impacts as far as they are achievable. This research is carried out in the cooperation with the company Fiedler Zden k Ltd. and under the support of the Technology Agency of the Czech Republic.

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Section: Middle-scale Boiler Optimization Algorithmmentioning

confidence: 99%

“…[9] - [12] The diagram of proposed boiler operating conditions and optimization impact evaluation algorithm is depicted in Fig. 8.…”

Section: Middle-scale Boiler Optimization Algorithmmentioning

confidence: 99%

Advanced control with economic - ecological optimization for biomass-fired boilers

Oswald

Plaček

Šulc

2014

Proceedings of the 2014 15th International Carpathian Control Conference (ICCC)

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“…The self-organizing map is famous as an effective visualization methods, with which high-dimensional input data can be reduced to 2-dimensional data space that can be interpreted and perceived more easily. SOM has been used in many real world problems ranging from analysis of complex data sets to monitoring of large industrial processes [8,9]. In Self-organizing Maps, the output layer of neurons is usually arranged in a 2-dimensional lattice structure.…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Self-Organizing Maps of Nutrition, Lifestyle and Health Situation in the World

Mehmood

Abbas

Chen

et al. 2011

Advances in Self-Organizing Maps

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Abstract. In this article, we present an analysis of the impact of nutrition and lifestyle on health at a global level. We have used Self-organizing Maps (SOM) algorithm as the analysis technique. SOM enables us to visualize the relative position of each country against a set of the variables related to nutrition, lifestyle and health. The positioning of the countries follows the basic understanding of their status with respect to their socioeconomic conditions. We have also studied the relationships between the variables supported by the SOM visualization. This analysis presents many obvious correlations but also some surprising findings that are worth further analyses.

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“…However, the complexity of the whole process, with several tightly coupled phenomena (such as chemical, mechanical, and thermal) makes it di cult to build an accurate model and moreover to tune its parameters. An approach to enhance the knowledge about complex processes is visualizing their relevant information, using dimensionality reduction (DR) techniques [4,5]. DR techniques allow to project and study the structure of high-dimensional data into a low-dimensional space, typically a 2D/3D for visualization purposes, improving the exploratory data analysis [6].…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis of a Cold Rolling Process Using Data-Based Modeling

Pérez

García-Fernández

Díaz

et al. 2012

Engineering Applications of Neural Networks

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Abstract. In this paper, a method to characterize the chatter phenomenon in a cold rolling process is proposed. This approach is based on obtaining a global nonlinear dynamical MISO model, relating four input variables and the exit strip thickness as the output variable. In a second stage, local linear models are obtained for all working points using sensitivity analysis on the nonlinear model to get input/output small signal models. Each local model is characterized by a high dimensional vector containing the frequency response functions (FRF) of the four SISO resulting models. Finally, the FRF's are projected on a 2D space, using the t-SNE algorithm, in order to visualize the dynamical changes of the process. Our results show a clear separation between chatter condition and other vibration states, allowing an early detection of chatter as well as being a visual analysis tool to study the chatter phenomenon.

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A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes

Cited by 39 publications

References 22 publications

Advanced control with economic - ecological optimization for biomass-fired boilers

Advanced control with economic - ecological optimization for biomass-fired boilers

Self-Organizing Maps of Nutrition, Lifestyle and Health Situation in the World

Visual Analysis of a Cold Rolling Process Using Data-Based Modeling

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