This paper investigates technique for solving spectrometry inverse problem the neural network as method for reconstruction of electron beam spectrum using depth-charge curve. The inverse problem turned into multivariable optimization and the form of spectrum is based on proposed three-parameter model. Radial basis function network calculates the parameters of this model. We developed computatio...

In this paper, we suggest a neural network signal detector using radial basis function (RBF) network. We employ this RBF Neural detector to detect the presence or absence of a known signal corrupted by different Gaussian, non-Gaussian and impulsive noise components. In case of non-Gaussian noise, experimental results show that RBF network signal detector has significant improvement in performan...

The paper presents the problem of using neural network for military vehicle classification on the basis of ground vibration. One of the main element of the system is a unit called geophone. This unit allows to measure amplitude of ground vibration in each direction for certain period of time. The value of amplitude is used to fix the characteristic frequencies of each vehicle. If we want to fix...

This paper describes a global approach to the construction of Radial Basis Function (RBF) neural net classifier. We used a new simple algorithm to completely define the structure of the RBF classifier. This algorithm has the major advantage to require only the training set (no step learning, threshold or other parameters as in other methods). Tests on several benchmark datasets showed, despite ...

A two-layer neural network is developed in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted logistic function with the mean of the partial input-weight similarities. The model is capable of dealing directly with variables of potentially different nature (continuous, ordinal, categorical); t...

precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

This work studies some of the approximating properties of feedforward neural networks as a function of the number of nodes. Two cases are considered: sigmoidal and radial basis function networks. Bounds for the approximation error are given. The methods through which we arrive at the bounds are constructive. The error studied is the L1 or sup error.

In this paper, we introduce a novel method of automatically finding the training set of RBF networks for morphing a prototype face to represent a new face. This is done by automatically specifying and adjusting corresponding feature points on a target face. The RBF networks are then used to transfer the muscles on the prototype face to the morphed face. The automatic adjusting of the feature po...