Neuro-Fuzzy Modeling for Multi-Objective Test Suite OptimizationRegression testingtest suite optimizationneuro-fuzzy modelingcomputational intelligenceRegression testing is a type of testing activity, which ensures that source code changes do not affect the unmodified portions of the software adversely. This ...
NEURO-FUZZY MODELING APPLIED IN PROGRAM MANAGEMENT TO INCREASE LOCAL PUBLIC ADMINISTRATION PERFORMANCE Annals of the University of Oradea, Economic Science SeriesZaharia-Rdulescu, Adrian-MihaiRadu, Ioan
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 ...
Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle This paper presents application of Adaptive Neuro-fuzzy inference system (ANFIS) into a squirrel cage induction machine towards modeling, control and estim... R Baz,K El Majdoub,F Giri,... - 《Indonesian Journal...
Malliga. A new approach of adaptive Neuro Fuzzy Inference System (ANFIS) modeling for yield prediction in the supply chain of Jatropha. IEEE 17th ... SP Srinivasan,P Malliga - IEEE International Conference on Industrial Engineering & Engineering Management 被引量: 6发表: 2010年 Adaptive Neuro-...
However, modeling data using neuro-fuzzy systems involves the contradictory requirements of interpretability versus accuracy. This monograph focuses on increasing interpretability without compromising accuracy using a novel hybrid intelligent Rough ... KA Kai - 《Vdm Verlag Dr Müller》 被引量: 14发表: ...
Two modeling techniques of microwave devices are proposed to generate neuro-fuzzy-based models. These techniques use the adaptive neuro-fuzzy inference system approach, which compensates the error between an initial coarse model and an electromagnetic simulator (or measurement data). The aim of these...
Top Abstract Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human...
The local paradigm for modeling and control: from neuro-fuzzy to lazy learning The composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This pa... G Bontempi,H Bersini,M Birattari - 《Fuzzy Sets & Systems...
Basics of Neuro-Fuzzy model Recursive-generalised-least-squares (RGLS) based learning algorithm Convergence of the proposed algorithm Numerical examples Experimental study Conclusion Data availability References Funding Author information Ethics declarations Additional information Proof of Theorem 1 Rights and per...