In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. The method provides an extra degree of flexibility to the kernel structure. This flexibility comes through the use of modifier functions applied to the distance computation procedure, essential for all kernel evaluations. Initially the classifier uses an unsupervised method to construct the network...

In order to guarantee security and stable operation of electric vehicle, it is necessary to on-line estimation for the state of charge (SOC) of batteries. The power battery is a complex nonlinear system, and Radial Basis Function Neural Network (RBF NN) has advantages in solving nonlinear problems, so the model of on-line SOC estimation based on RBF NN is proposed. In order to improve the predi...

A signal su ers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, nonlinear equalizers have the potential to compensate for all three sources of channel distortion. Previous authors have shown that nonlinear feedforward equalizers based on either mul...

This paper presents a novel approach in learning algorithms commonly used for training radial basis function (RBF) neural networks. This approach could be used in applications that need real-time capabilities for retraining RBF neural networks. The proposed method is a Three-Phase Learning Algorithm that optimizes the functionality of the Optimum Steepest Decent (OSD) learning method. RBF neura...

These kernels combine the benefits of two other important classes of kernels: the homogeneous additive kernels (e.g. the χ2 kernel) and the RBF kernels (e.g. the exponential kernel). However, large scale problems require machine learning techniques of at most linear complexity and these are usually limited to linear kernels. Recently, Maji and Berg [2] and Vedaldi and Zisserman [4] proposed exp...

We have shown that duct modeling using the generalized RBF neural network (DM RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM RBF), at first the duct is identified using a generalized RBF network, after that N stage of time delay of the input signal to th...

In this paper we propose a radial basis function (RBF) neural network for nonlinear time-invariant channel equalizer. The RBF network model has a three-layer structure which is comprised of an input layer, a hidden layer and an output layer. The learning algorithm consists of unsupervised learning and supervised learning. The unsupervised learning mainly adjusts the weight among input layer and...

Approximation methods are widely used in many fields and many techniques have been published already. This comparative study presents a comparison of LOWESS (Locally weighted scatterplot smoothing) and RBF (Radial Basis Functions) approximation methods on noisy data as they use different approaches. The RBF approach is generally convenient for high dimensional scattered data sets. The LOWESS me...