estimation of reservoir water saturation (sw) is one of the main tasks in well logging. many empirical equations are available, which are, more or less, based on archie equation. the present study is an application of radial basis function neural network (rbfnn) modeling for estimation of water saturation responses in a carbonate reservoir. four conventional petrophysical logs (pls) including d...

Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In ...

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...

In this paper we propose a strategy to shape adaptive radial basis functions through potential functions. DYPOF (DYnamic POtential Functions) neural network (NN) is designed based on radial basis functions (RBF) NN with a two-stage training procedure. Static (fixed number of RBF) and dynamic (ability to add or delete one or more RBF) versions of our learning algorithm are introduced. We investi...

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

This paper proposes an artificial neural network RBF to classification using feature descriptors. The theoretical and practical aspects of theory F distributions with different degrees of freedom introduced. The distribution F densities are similar in shape, making it difficult to identify the differences between the two densities. This paper is concerned with separating these same probability ...

Monitoring lightning electromagnetic pulses (sferics) and other terrestrial as well as extraterrestrial transient radiation signals is of considerable interest for practical and theoretical purposes in astroand geophysics as well as meteorology. Managing a continuous flow of data, automisation of the detection and classification process is important. Features based on a combination of wavelet a...

| This paper presents the Time Delay Radial Basis Function Network (TDRBF) for recognition of pho-nemes. The TDRBF combines features from Time Delay Neural Networks (TDNN) and Radial Basis Functions (RBF). The ability to detect acoustic features and their temporal relationship independent of position in time is inherited from TDNN. The use of RBFs leads to shorter training times and less parame...

This paper presents an easy to use constructive training algorithm for Probabilis tic Neural Networks a special type of Radial Basis Function Networks In contrast to other algorithms prede nition of the network topology is not required The pro posed algorithm introduces new hidden units whenever necessary and adjusts the shape of already existing units individually to minimize the risk of miscl...