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 ...

| Adaptive methods for performing multiuser demodula-tion in a Direct-Sequence Spread-Spectrum Multiple-Access (DS/SSMA) communication environment are investigated. In this scenario, the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. This ...

It’s well known that the computing time to train multilayer perceptrons is very long because of weight space of the neural networks and small amount of adjustment of the wiights for convergence. The matter becomes worse when the size of training data set is large, which is common in data mining tasks. Moreover, depending on samples, the performance of neural networks change. So, in order to det...

The paper examines applicability of Hopfield Model (HFM) for weather forecasting in southern Saskatchewan, Canada. The model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN) and radial basis function network (RBFN). The data of temperature, wind speed and relative humidity were used to train and test the four models. With each model, ...

A feedback simulation model based on radial basis function neural networks is newly developed in this research to analyse the interaction between urban densities and travel mode split. The changes of populations, road mileages, travel mode split, and so on of the enlarging urbanized areas of different cities in China are studied for the trainings of the radial basis function neural networks con...

Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is eas...

L is one of the many types of cancers. Leukemia is caused in the white blood cells near the bone marrow region of our body. In this the white blood cells (WBCs) which get infected turns blue. Like any other cancer in this also the cell divides itself at the faster pace. Even when it is not required they multiply causing a tumor. Detected and treated at an early stage of leukemia saves a lot of ...