Evidential clustering is an approach to based on the use of Dempster-Shafer mass functions represent cluster-membership uncertainty. In this paper, we introduce a neural-network evidential algorithm, called NN-EVCLUS, which learns mapping from attribute vectors functions, in such way that more similar inputs are mapped output with lower degree conflict. The neural network can be paired one-clas...

Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...

the biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. so far, many computational and statistical methods such as pca and ica have been employed for computing low-dimensional or hidden representat...

In this paper we have used a Neural Networks approach for the estimation of the Round Trip Time (RTT) in the TCP protocol. We constructed a network operation scenario that approaches the Internet operation. By observing the traffic between different nodes we collected the RTT values during normal network operation. Then we organized these values in such a way so that part of them was used as a ...

In this paper, we report the results of Marathi (Language spoken in the state of Maharashtra, India) spoken digit recognition using General Fuzzy Min-Max Neural Network (GFMM NN)[1] and Modified General Fuzzy MinMax Neural Network (MGFMM NN), which is obtained by modifying the transfer function of output layer of GFMM NN.

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

The identification of nonlinear systems by artificial neural networks has been successfully applied in many applications. In this context, the radial basis function neural network (RBF-NN) is a powerful approach for nonlinear identification. A RBF neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain Gaussian transfer functions whose outp...

Stock market prediction is forever important issue for investor. Computer science plays vital role to solve this problem. From the evolution of machine learning, people from this area are busy to solve this problem effectively. Many different techniques are used to build predicting system. This research describes different state of the art techniques used for stock forecasting and compare them ...

A fault diagnosis system in a multilevel-inverter using a compact neural network is proposed in this paper. It is difficult to diagnose a multilevel-inverter drive (MLID) system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network classification is applied to the fault diagnosis of a MLI...