An empirical approach that makes use of neuro-fuzzy synergism to evaluate the students in the context of an intelligent tutoring system is presented. In this way, a qualitative model of the student is generated, which is able to evaluate information regarding student's knowledge and cognitive abilities in a domain area. The neuro-fuzzy model has been tested on a prototype tutoring system in the...

the fuzzy linear regression model with fuzzy input-output data andcrisp coefficients is studied in this paper. a linear programmingmodel based on goal programming is proposed to calculate theregression coefficients. in contrast with most of the previous works, theproposed model takes into account the centers of fuzzy data as animportant feature as well as their spreads in the procedure ofconstr...

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold ...

A piecewise linear (PWL) function approximation scheme is described by a lattice algebra of modified operators that allows for the interpolation of PWL function vertexes. A new recursive method called Centred Recursive Interpolation (CRI) based on such modified operators is analysed for successive function smoothing and more accurate approximation. This approximation method, simple but accurate...

This paper proposes the application of Genetic Learning as a procedure for the optimal design and training of neuro-fuzzy systems. Once this learning procedure has been implemented, hybridization between Genetic Algorithms (GA) and the traditional local search technique is carried out to form a Hybrid Algorithm, in order to achieve the maximum possible efficiency in the search, and to be able t...

Weather prediction is an ever challenging area of investigation for scientists. The Adaptive Neuro-Fuzzy Inference System (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different ki...

This paper surveys Neuro fuzzy systems (NFS) development in biomedical field. Paper gives brief literature review of articles for last decade (2005-2015) which explores various Neuro Fuzzy System methodologies that have been developed during this period of time, their work done and deficiencies. Use of Neuro fuzzy integrated systems in various biomedical engineering applications is summarised. ...

A neuro-fuzzy modeling for forecasting the future dynamical behavior in vibration testing during satellite qualification is proposed in this paper. Vibration testing is employed for emulating vibrations present during the lifetime launching. There are different levels of excitation during vibration testing in order to verify and assure that the satellite and their sub-systems will support the e...

In a fuzzy metric space (X;M; *), where * is a continuous t-norm,a locally fuzzy contraction mapping is de ned. It is proved that any locally fuzzy contraction mapping is a global fuzzy contractive. Also, if f satis es the locally fuzzy contractivity condition then it satis es the global fuzzy contrac-tivity condition.    

In this paper, the robust neuro-fuzzy networks (RNFNs) are proposed to improve the problems of neuro-fuzzy networks (NFNs) for modeling with outliers. Firstly, the support vector regression (SVR) approach is applied to obtain the initial structure of RNFNs. Because of the SVR approach is equivalent to solving a linear constrained quadratic programming problem under the fixed structure of SVR, t...