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...

The clustering algorithm hybridization scheme has become of research interest in data partitioning applications in recent years. The present paper proposes a Hybrid Fuzzy clustering algorithm (combination of Fuzzy C-means with extension and Subtractive clustering algorithm) for data classifications applications. The fuzzy c-means (FCM) and subtractive clustering (SC) algorithm has been widely d...

The design of a support vector machine with Gaussian kernels is considered for modeling nonlinear processes. The structure is equivalent to a neuro-fuzzy system based on radial basis function network considering some restrictions. To improve the interpretability and reduce the complexity of the structure a hybrid learning scheme is proposed. First, the input-output data is supervised clustered ...

This article presents a study of academic performance evaluation using soft computing techniques inspired by the successful application of K-means, fuzzy C-means (FCM), subtractive clustering (SC), hybrid subtractive clustering-fuzzy C-means (SC-FCM) and hybrid subtractive clustering-adaptive neuro fuzzy inference system (SC-ANFIS) methods for solving academic performance evaluation problems. M...

This project consists of two parts. The first part is a general review of the previous and current research on human face recognition, including initial motivation, approaches, major problems and solutions, etc. The second part propose a new method for learning of radial basis function (RBF) neural networks which is based on subtractive clustering algorithm(SCA) and its application to face reco...

-In order to overcome the defects of the traditional static load modeling in coalmine, a new modeling method is proposed in this paper. First, a new clustering method based on improved PSO algorithm is presented to classify the load data in order to reduce the number of load model before modeling. Second, RBF neural network based on improved PSO algorithm is put forward to establish aggregate l...

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 ...

In this paper, a comparative study of classification of the analog modulated communication signals using clustering techniques is introduced. Four different clustering algorithms are implemented for classifying the analog signals. These clustering techniques are K-means clustering, fuzzy c-means clustering, mountain clustering and subtractive clustering. Two key features are used for characteri...