On Global–Local Artificial Neural Networks for Function Approximation

On global-local artificial neural networks for function approximation

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate ide...

Function Approximation Using Artificial Neural Networks

Function approximation, which finds the underlying relationship from a given finite input-output data is the fundamental problem in a vast majority of real world applications, such as prediction, pattern recognition, data mining and classification. Various methods have been developed to address this problem, where one of them is by using artificial neural networks. In this paper, the radial bas...

Approximation of Conditional Probability Function Using Artificial Neural Networks

This work shows that a multilayer perceptron can successfully be applied for approximation of conditional probability function. Such an approach makes it possible to examine the training sample with contradictory vectors and to interpret the outputs of the network in classification tasks as the evaluation of the probability of belonging to the specific class. This method was used for creating t...

Geoid Determination Based on Log Sigmoid Function of Artificial Neural Networks: (A case Study: Iran)

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensityvalues of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“BPANN”, and “collocation” ...

Intelligent Systems: Approximation by Artificial Neural Networks