Outlier detection has many important applications in the field of fraud detection, network robustness analysis and intrusion detection. Some researches have utilized the neural network to solve the problem because it has the advantage of powerful modeling ability. In this paper, we propose a RBF neural network model using subtractive clustering algorithm for selecting the hidden node centers, w...

The estimation of crop chlorophyll content could provide technical support for precision agriculture. Canopy spectral reflectance was simulated for different chlorophyll levels using radiative transfer models. Then with multiperiod measured hyperspectral data and corresponding chlorophyll content, after extracting six wavelet energy coefficients from the responded bands, an evaluation of soybea...

A new recursively self-constructing fractal learning algorithm for an artificial neural network is presented. The network architecture is based on a tree-like structure of Radial Basis Function (RBF) networks. This paper examines some of the problems with RBF nets, and suggests a possible solution. Neurons in RBF nets have local receptive fields. The new learning algorithm ‘grows’ new sub-netwo...

Because simulation turntable servo system is highly nonlinear and uncertainty plants, a fuzzy neural network PID controller is proposed based on the Radial Basis Function (RBF). Up to now, various kinds of nonlinear PID controllers have been designed in order to satisfactorily control this system and some of them applied in actual systems with different degrees. Given this background, the step ...

We present a network design that provides flexible and policy-compliant forwarding. Our proposal centers around a new architectural concept: that of packet rules. A rule is a simple if-then-else construct that describes the manner in which the network should – or should not – forward packets. A packet identifies the rule by which it is to be forwarded and routers forward each packet in accordan...

Debris flow is one of the major threats for the sustainability of environmental and social development. The velocity directly determines the impact on the vulnerability. This study focuses on an approach using radial basis function (RBF) neural network and gravitational search algorithm (GSA) for predicting debris flow velocity. A total of 50 debris flow events were investigated in the Jiangjia...

With the popularization of intelligent manufacturing, manipulator has found ever wider application in various industries. A requires a real-time and fast control algorithm order to improve accuracy all kinds precise operations. This paper proposes an based on adaptive radial basis function (RBF) for approximating parameters manipulator, equations are designed automatically adjust weight RBF. Pr...

This study presents a new hybrid algorithm for training RBF network. The algorithm consists of a proposed clustering algorithm to position the RBF centres and Givens least squares to estimate the weights. This paper begins with a discussion about the problems of clustering for positioning RBF centres. Then a clustering algorithm called moving k-means clustering algorithm was proposed to reduce ...

Convolutional Radial Basis Function (RBF) networks are introduced for smoothing out irregularly sampled signals. Our proposed technique involves training a RBF network and then convolving it with a Gaussian smoothing kernel in an analytical manner. Since the convolution results in an analytic form, the computation necessary for numerical convolution is avoided. Convolutional RBF networks need t...