A quick guided tour to the modal logic S4.2

Chalki, Aggeliki; Koutras, Costas D.; Zikos, Yorgos

doi:10.1093/jigpal/jzy008

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…For the behavior prediction, accomplishing the process modeling is mandatory. Some of the possible ways for this purpose come from Multiple Regression Analysis (MRA)-based models [24], which have very common limitations in several instances [25]. This problem is due to the possible nonlinearities in most of the cases [26].…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

et al. 2019

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The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of 3.73 with the validation dataset.

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Section: Introductionmentioning

confidence: 99%

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

et al. 2019

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“…These kinds of models were used with good results in several different applications [36][37][38][39][40][41][42]. Other algorithms based on multiple regression analysis used to have worse performance, but they are still used [37,[43][44][45][46]. The hybrid system presented in this paper uses Artificial Neural Networks and Polynomial Regression techniques to perform the regression phase [47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand

et al. 2019

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Hydrogen-based energy storage and generation is an increasingly used technology, especially in renewable systems because they are non-polluting devices. Fuel cells are complex nonlinear systems, so a good model is required to establish efficient control strategies. This paper presents a hybrid model to predict the variation of H2 flow of a hydrogen fuel cell. This model combining clusters’ techniques to get multiple Artificial Neural Networks models whose results are merged by Polynomial Regression algorithms to obtain a more accurate estimate. The model proposed in this article use the power generated by the fuel cell, the hydrogen inlet flow, and the desired power variation, to predict the necessary variation of the hydrogen flow that allows the stack to reach the desired working point. The proposed algorithm has been tested on a real proton exchange membrane fuel cell, and the results show a great precision of the model, so that it can be very useful to improve the efficiency of the fuel cell system.

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