• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.1307-1319
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• 2018

As interests in the air pollution increases, many kinds of researches are underway on the reduction of air pollutants. The removal of nitrogen oxides from the emission gas of diesel vehicles using urea solution has shown a great effect. The quality of urea solution is strictly defined by domestic law, but the increase of impurities in urea solution reduces the effect of reducing nitrogen oxides. Therefore, in this study, the change of physical properties of urea solution was analyzed after heating the urea solution for a certain temperature and time. Also, the changes of physical properties of urea solution were analyzed according to kinds of storage container and temperature for storing the urea solution. After heating the urea solution for a certain period of time, the biuret content in urea solution increased and the content of urea decreased. As the urea content decreased, both density and refractive index decreased. In the storage stability test carried out at a constant temperature with iron and PET containers, no change in physical properties was observed.