Clustering is one of the most fundamental algorithms which have got huge applications especially in the area of Neuro Fuzzy Systems, Data Analysis, Linear Vector Quantization, Bio-informatics etc. Various approaches exist for clustering of data. A few of the commonly used approaches are K-Means clustering, Fuzzy C-Means Clustering, Subtractive Clustering, etc. Clustering may involve varied uses...

This paper presents clustering techniques (K-means, Fuzzy K-means, Subtractive) applied on specific databases (Flower Classification and Mackey-Glass time series) , to automatically process large volumes of raw data, to identify the most relevant and significative patterns in pattern recognition, to extract production rules using Mamdani and Takagi-SugenoKang fuzzy logic inference system types.

A new algorithm for training radial basis function neural networks is presented in this paper. The algorithm, which is based on the subtractive clustering technique, has a number of advantages compared to the traditional learning algorithms, including faster training times and more accurate predictions. Due to these advantages the method proves suitable for developing discrete-time models for c...

Radioactivity is the spontaneous emission of energy from unstable atoms. Radioactive sources have radionuclides. Radionuclide undergoes radioactive decay and emits gamma rays and subatomic particles, constituting the ionizing radiation. The gamma ray energy of a radionuclide is used to determine the identity of gamma emitters present in the source. This paper describes the hardware implementati...

This paper presents a comparative study between three versions of adaptive neuro-fuzzy inference system (ANFIS) algorithms and a pseudo-forward equation (PFE) to characterize the North Sea reservoir (F3 block) based on seismic data. According to the statistical studies, four attributes (energy, envelope, spectral decomposition and similarity) are known to be useful as fundamental attributes in ...

Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  First,...

Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attract...