STLF in the user-side for an iEMS based on evolutionary training of Adaptive Networks

Cárdenas, Juan José; Giacometto, F.; García, A.; Romeral, J.L.

doi:10.1109/etfa.2012.6489626

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…This method fuses the parametric adaptability of ANN, and the generalization capabilities of the fuzzy logic. ANFIS based prognostic systems offers a very reliable and robust condition predictor, since it can capture the system dynamic behavior quickly and accurately [17][18][19][20].…”

Section: B Adaptive Network Based Fuzzy Inference Systemmentioning

confidence: 99%

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Zurita

Carino

Sala

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

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Section: B Adaptive Network Based Fuzzy Inference Systemmentioning

confidence: 99%

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Zurita

Carino

Sala

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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Section: Adaptive Network Based Fuzzy Inference Systemmentioning

confidence: 99%

Distributed neuro-fuzzy feature forecasting approach for condition monitoring

Zurita

Carino

Delgado

et al. 2014

Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)

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The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.Postprint (published version

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“…An adaptive network that forecasts electrical consumptions on a manufacturing plant is shown in [3] as a part of an iEMS. An iEMS that monitors and adjusts energy supply and demand in real-time, in order to form the best combination, was implemented in [4], with the ability to predict the electric power demand based on historical data.…”

Section: Introductionmentioning

confidence: 99%

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

Sala

Kampouropoulos

Giacometto

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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A model of power demand represents the foundation of any intelligent Energy Management System, and its accuracy is the key factor determining the performance of such system. In order to improve the accuracy of the modeling process, a multi-model approach based on a Hierarchical Clustering of similar load behaviors is presented. The clustering algorithm joins similar data subsets in groups that are modelled separately using Adaptive Neuro-Fuzzy Inference Systems. Thus, each of the obtained models addresses only the characterization of one behavior, which provides better accuracy than classical approaches based on a single model, in addition to being easier and faster to train. During the training process of the models, an input selection technique based on Genetic Algorithms is proposed to search and select the best combination of inputs. The use of search algorithms allows to reduce the complexity of this task while maintaining the system performance, which represents a significant time saving of expert staff. The proposed approach is validated by means of experimental data from an automotive manufacturing plant. In addition to improving the forecasting accuracy, this methodology automates the segmentation of the load profiles into models and the selection of their inputs, as well as improving parallelization to effectively reduce the computation time.Keywords-adaptive neuro-fuzzy inference systems; demand side management; genetic algorithms; hierarchical clustering; intelligent energy management system.

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STLF in the user-side for an iEMS based on evolutionary training of Adaptive Networks

Cited by 8 publications

References 19 publications

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Distributed neuro-fuzzy feature forecasting approach for condition monitoring

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

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