An e cient procedure is proposed for initializing two-layer perceptrons and for determining the optimal number of hidden neurons. This is based on the Orthogonal Least Squares method, which is typical of RBF as well as Wavelet networks. Some experiments are discussed, in which the proposed method is coupled with standard backpropagation training and compared with random initialization.

Abstract The growth of urbanization in Klang District was considered to be fast and has increased the concern policy makers town planners. This paper assess changes urban development using Support Vector Machine (SVM) classification by different kernel for purpose studying built up area within year 2017 2021. At initial stage image processing, Land Use Cover (LULC) been classified based on use ...

The leakage of the ship’s pipeline system will bring great risks to engine equipment and seriously threaten vitality ship. In this paper, detection localization research are carried out based on vibration signal generated by leakage. First, finite element model is constructed obtain variation law when leaks out. Second, processed variational mode decomposition (VMD) radial basis function (RBF) ...

----In statistical practices, difficulties of missing data are universal. Several techniques are used to handle this dilemma of missing data. They include both old approaches, which require only a small amount of mathematical computations and new approaches, which require additional difficult computations that are ever easier for social work researchers to carry out the statistical programming ...

Predicting reservoir water levels helps manage droughts and floods. level is complex because it depends on factors such as climate parameters human intervention. Therefore, predicting needs robust models. Our study introduces a new model for levels. An extreme learning machine, the multi-kernel least square support vector machine (MKLSSVM), developed to predict of in Malaysia. The also novel op...

This paper proposes a four stage, denoising, feature extraction, optimization and classification method for detection of premature ventricular contractions. In the first stage, we investigate the application of wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In this stage, the Stationary Wavelet Transform is used. Feature extraction module extracts ten ECG mor...

For high-dimensional data classification such as hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted featur...

We construct genRBF kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data into the function space and to define a kernel between two missing data points based...

Radial Basis Function (RBF) interpolation is a common approach to scattered data interpolation. Gaussian Process regression is also a common approach to estimating statistical data. Both techniques play a central role, for example, in statistical or machine learning, and recently they have been increasingly applied in other fields such as computer graphics. In this survey we describe the formul...

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series,...