Abstract: This paper is concerned with knowledge representation
and extraction of fuzzy if-then rules using Interval Type-2
Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of
fuzzy granulation. This proposed clustering algorithm is based on
information granulation in the form of IT2 based Fuzzy C-Means
(IT2-FCM) clustering and estimates the cluster centers by preserving
the homogeneity between the clustered patterns from the IT2 contexts
produced in the output space. Furthermore, we can obtain the
automatic knowledge representation in the design of Radial Basis
Function Networks (RBFN), Linguistic Model (LM), and Adaptive
Neuro-Fuzzy Networks (ANFN) from the numerical input-output data
pairs. We shall focus on a design of ANFN in this paper. The
experimental results on an estimation problem of energy performance
reveal that the proposed method showed a good knowledge
representation and performance in comparison with the previous
works.
Abstract: Traffic management in an urban area is highly facilitated by the knowledge of the traffic conditions in every street or highway involved in the vehicular mobility system. Aim of the paper is to propose a neuro-fuzzy approach able to compute the main parameters of a traffic system, i.e., car density, velocity and flow, by using the images collected by the web-cams located at the crossroads of the traffic network. The performances of this approach encourage its application when the traffic system is far from the saturation. A fuzzy model is also outlined to evaluate when it is suitable to use more accurate, even if more time consuming, algorithms for measuring traffic conditions near to saturation.
Abstract: In this research study, an intelligent detection system
to support medical diagnosis and detection of abnormal lesions by
processing endoscopic images is presented. The images used in this
study have been obtained using the M2A Swallowable Imaging
Capsule - a patented, video color-imaging disposable capsule.
Schemes have been developed to extract texture features from the
fuzzy texture spectra in the chromatic and achromatic domains for a
selected region of interest from each color component histogram of
endoscopic images. The implementation of an advanced fuzzy
inference neural network which combines fuzzy systems and
artificial neural networks and the concept of fusion of multiple
classifiers dedicated to specific feature parameters have been also
adopted in this paper. The achieved high detection accuracy of the
proposed system has provided thus an indication that such intelligent
schemes could be used as a supplementary diagnostic tool in
endoscopy.
Abstract: The new technology of fuzzy neural networks for identification of parameters for mathematical models of geofields is proposed and checked. The effectiveness of that soft computing technology is demonstrated, especially in the early stage of modeling, when the information is uncertain and limited.