This paper introduces a novel method for human face recognition that employs a set of different kind of features from the face images with Radial Basis Function (RBF) neural network called the Hybrid N-Feature Neural Network (HNFNN) human face recognition system. The face image is projected in each appropriately selected transform methods in parallel. The output of the RBF classifiers are fused...

In this paper, we investigate the decision making ability of a fully complex-valued radial basis function (FC-RBF) network in solving real-valued classification problems. The FC-RBF classifier is a single hidden layer fully complex-valued neural network with a nonlinear input layer, a nonlinear hidden layer, and a linear output layer. The neurons in the input layer of the classifier employ the ...

Query expansion methods have been extensively studied in information retrieval. This paper proposes a query expansion method. The HQE method employs a combination of ontology-based collaborative filtering and neural networks to improve query expansion. In the HQE method, ontology-based collaborative filtering is used to analyze semantic relationships in order to find the similar users, and the ...

ÐThis is an elementary research for assigning color values to voxels of multichannel Magnetic Resonance Imaging (MRI) volume data. The MRI volume data sets obtained under different scanning conditions are transformed to the components by independent component analysis (ICA), which enhances physical characteristics of the tissue. The transfer functions for generating color values from independen...

This paper presents a set of optimizations in learning algorithms commonly used for training radial basis function neural networks. These optimizations are applied to a RBF neural network used in identifying helicopter types processing their rotor sounds. The first method uses an optimum learning rate in each iteration of train process. This method increases the speed of learning process and al...

This paper presents FC networks that are instantaneously trained neural networks that allow rapid learning of non-binary data. These networks, which generalize the earlier CC networks, have been compared against Backpropagation (BP) and Radial Basis Function (RBF) networks and are seen to have excellent performance for prediction of time-series and pattern recognition. The networks can generali...

Neural networks are often used as a powerful discriminating estimator for tasks in system identification. This paper describes a neural-network-based method relies on the Radial Basis Function Network (RBF network), for estimating the variable damping factor C (n) and spring constant K (n) of a weighting platform. Firstly, the RBF network learns key properties of the step response of the weight...

Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles Anugrah K. Pamosoaji a , Pham Thuong Cat b & Keum-Shik Hong a c a School of Mechanical Engineering, Pusan National University, Busan, Korea b Department of Automation Technology, Institute of Information Technology, Hanoi, Vietnam c Department of Cogno-Mec...

This paper presents a novel algorithm that modifies the speech uttered by a source speaker to sound as if produced by a target speaker. In particular, we address the issue of transformation of the vocal tract characteristics from one speaker to another. The approach is based on estimating spectral envelopes using radial basis function (RBF) networks, which is one of the well-known models of art...

Partial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geomerty have been succesfuly treated using sigmoidal multilayer perceptrons in previous works [1, 2]. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a reasonabl...