Neuro fuzzy model for predicting the dynamic characteristics of beams[J]. Acta Mechanica Solida Sinica, 2014, 27 (1): 85-96.Bachi I.O., Abdulrazzaq N., He Z. 2014. NEURO FUZZY MODEL FOR PREDICTING THE DYNAMIC CHARACTERISTICS OF BEAMS. Acta Mechanica Solida Sinica. Vol. 27. No. 1, ...
Model identification of nonlinear time varying dynamic systems is challenging because the system behaviours may vary significantly in different operational
Shalini BhatiaShreeja RKHUSHALI DEULKAREngg Journals PublicationInternational Journal of Engineering Science & TechnologyShreeja R. and D. K., Neuro Fuzzy Model for Face Recognition with Curvelet based Feature Image International Journal of Engineering Science and Technology (IJEST), 2011. 3(6)....
This paper presents a reduced-order modeling approach based on recurrent local linear neurofuzzy models for predicting generalized aerodynamic forces in the time domain. Regarding aeroelastic applications, the unsteady aerodynamic loads are modeled as a nonlinear function of structural eigenmode-based disturb...
Modeling and control of ventilation and heating system using neuro-fuzzy inference systemDealing with the nonlinearities and uncertainties in Ventilation and Heating System are the main challenges in developing a reliable model for the system... NQ Zaudi 被引量: 0发表: 2015年 Trends and Issues in...
This study investigates the feasibility of a neuro-fuzzy (NF) approach for the modelling of shear strength of reinforced concrete (RC) beams without web reinforcement. The proposed NF model is based on a wide range of experimental data (664 tests) gathered from the literature from 56 separate ...
Simplifying a neuro-fuzzy model作者:G. Castellano, A. M. Fanelli 摘要 Neuro-fuzzy modeling allows a fuzzy system to be refined by neural training, thus avoiding lenghty trial-and-error phases in defining both membership functions and inference rules. An approach to obtain simple neuro-fuzzy ...
The fuzzy logic is used for the regression test suite optimization in [3, 6, 20, 53]. We used the implementation of [6] to validate our approach. The Sugeno model for the RTO is given in Figure 22. The other details of the experiment can be seen in [6]. Figure 22: Sugeno Model...
We use the Adaptive Neuro-Fuzzy Inference System (ANFIS). The input to ANFIS are several parameters derived from the crop growth simulation model (CGMS) including soil moisture content, above ground biomass, and storage organs biomass. In addition we use remote sensing information in the form of...
respectively. A significant result of 92.91% obtained with the Adaptive Neuro-Fuzzy Inference System (ANFIS) model showed that it is most efficient in predicting, detecting and recovering stolen vehicles as compared with other machine learning algorithms such as Random Tree, Naïve Bayes, J48 and ...