In lots of data based prediction or modeling applications,uncertainties and/or noises in the observed data cannot be avoided.In such cases,it is more preferable and reasonable to provide linguistic(fuzzy)predicted res...In lots of data based prediction or modeling applications,uncertainties and/or noises in the observed data cannot be avoided.In such cases,it is more preferable and reasonable to provide linguistic(fuzzy)predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers.Linguistic dynamic system(LDS)provides a powerful tool for yielding linguistic(fuzzy)results.However,it is still difficult to construct LDS models from observed data.To solve this issue,this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas.Then,a hybrid learning method is proposed to construct the data-driven LDS model.The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method,then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules,and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets.The proposed approach is successfully applied to three real-world prediction applications which are:prediction of energy consumption of a building,forecasting of the traffic flow,and prediction of the wind speed.Simulation results show that the uncertainties in the data can be effectively captured by the linguistic(fuzzy)estimates.It can also be extended to some other prediction or modeling problems,in which observed data have high levels of uncertainties.展开更多
(Ba, Sr)RuO3 has been paid an attention as a promising electrode for (Ba, Sr)TiO3 dielectric material due to its similarity in structure and chemical composition with BST. In this study, (Ba, Sr)RuO3 conductive oxide ...(Ba, Sr)RuO3 has been paid an attention as a promising electrode for (Ba, Sr)TiO3 dielectric material due to its similarity in structure and chemical composition with BST. In this study, (Ba, Sr)RuO3 conductive oxide film was deposited on a 4 inch p-type Si wafer by metal organic chemical vapor deposition (MOCVD) using single cocktail source for the practical device application. Ba(TMHD)2, Sr(TMHD)2, Ru(TMHD)3 precursors and solvent [1-EtylePiPerdine (C7H15 N) ] as starting materials were mixed together for single cocktail source. A liquid delivery system (LDS) and a vaporization cell were utilized for the delivery and vaporization of single cocktail source, respectively. The source feeding rate was controlled by a liquid mass flow controller (LMFC). Deposition parameters, such as the oxygen flow and the source flow rate,were sensitive to phase formation, resistivity and the composition ratio of (Ba, Sr)RuO3 films. Highly (110)-textured (Ba,Sr)RuO3 film was obtained vhen the Ar/O2 ratio was 200/140 sccm at a source flow rate of 0.05 sccm. The process window of stoichiometric composition of BSR film was observed with varying the source flow rate from 0.05 sccm to 0.1 sccm.展开更多
基金supported by the National Natural Science Foundation of China(61473176,61773246)the Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities(ZR2015JL021)the Taishan Scholar Project of Shandong Province(TSQN201812092)
文摘In lots of data based prediction or modeling applications,uncertainties and/or noises in the observed data cannot be avoided.In such cases,it is more preferable and reasonable to provide linguistic(fuzzy)predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers.Linguistic dynamic system(LDS)provides a powerful tool for yielding linguistic(fuzzy)results.However,it is still difficult to construct LDS models from observed data.To solve this issue,this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas.Then,a hybrid learning method is proposed to construct the data-driven LDS model.The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method,then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules,and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets.The proposed approach is successfully applied to three real-world prediction applications which are:prediction of energy consumption of a building,forecasting of the traffic flow,and prediction of the wind speed.Simulation results show that the uncertainties in the data can be effectively captured by the linguistic(fuzzy)estimates.It can also be extended to some other prediction or modeling problems,in which observed data have high levels of uncertainties.
文摘(Ba, Sr)RuO3 has been paid an attention as a promising electrode for (Ba, Sr)TiO3 dielectric material due to its similarity in structure and chemical composition with BST. In this study, (Ba, Sr)RuO3 conductive oxide film was deposited on a 4 inch p-type Si wafer by metal organic chemical vapor deposition (MOCVD) using single cocktail source for the practical device application. Ba(TMHD)2, Sr(TMHD)2, Ru(TMHD)3 precursors and solvent [1-EtylePiPerdine (C7H15 N) ] as starting materials were mixed together for single cocktail source. A liquid delivery system (LDS) and a vaporization cell were utilized for the delivery and vaporization of single cocktail source, respectively. The source feeding rate was controlled by a liquid mass flow controller (LMFC). Deposition parameters, such as the oxygen flow and the source flow rate,were sensitive to phase formation, resistivity and the composition ratio of (Ba, Sr)RuO3 films. Highly (110)-textured (Ba,Sr)RuO3 film was obtained vhen the Ar/O2 ratio was 200/140 sccm at a source flow rate of 0.05 sccm. The process window of stoichiometric composition of BSR film was observed with varying the source flow rate from 0.05 sccm to 0.1 sccm.
