estimation (forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. thus, accuracy of the estimation is highly desirable. hibrid regression neural network is an approach proposed in this paper to obtain better fitness in comparison with regression analysis and the neural network methods. comparing the estimated resul...

Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879–892] has recently proposed an incremental extreme learning machine (I-ELM), which randomly adds hidden nodes incrementally and analytically determines the output weights. Although hidden nodes are generated randomly, the network constru...

Connections between spline approximation, approximation with rational functions, and feedforward neural networks are studied. The potential improvement in the degree of approximation in going from single to two hidden layer networks is examined. Some results of Birman and Solomjak regarding the degree of approximation achievable when knot positions are chosen on the basis of the probability dis...

The paper gives several strong results on neural network representation in an explicit form. Under very mild conditions a functional defined on a compact set in C[a, b] or L(p)[a, b], spaces of infinite dimensions, can be approximated arbitrarily well by a neural network with one hidden layer. The results are a significant development beyond earlier work, where theorems of approximating continu...

This paper considers mainly approximation by ridge functions. Fix a point a 2 IR n and a function g : IR ! IR. Then the function f : IR n ! IR deened by f (x) = g(ax), x 2 IR n , is a ridge or plane wave function. A sigmoidal function is a particular example of the function g which closely resembles 1 at 1 and 0 at ?1. This paper discusses approximation problems involving general ridge function...

We introduce a new procedure for training of artificial neural networks by using the approximation of an objective function by arithmetic mean of an ensemble of selected randomly generated neural networks, and apply this procedure to the classification (or pattern recognition) problem. This approach differs from the standard one based on the optimization theory. In particular, any neural networ...

We introduce a construction of a uniform measure over a functional class B which is similar to a Besov class with smoothness index r. We then consider the problem of approximating B using a manifold Mn which consists of all linear manifolds spanned by n ridge functions, i.e., Mn=[ i=1 gi(ai } x) : ai # S , gi # L2([&1, 1])], x # Bd. It is proved that for some subset A/Br of probabilistic measur...

Model complexity of feedforward neural networks is studied in terms of rates of variable-basis approximation. Sets of functions, for which the errors in approximation by neural networks with n hidden units converge to zero geometrically fast with increasing number n, are described. However, the geometric speed of convergence depends on parameters, which are specific for each function to be appr...

In this article we present a class of position control schemes for robot manipulators based on feedback of visual information processed through artificial neural networks. We exploit the approximation capabilities of neural networks to avoid the computation of the robot inverse kinematics as well as the inverse task space camera mapping which involves tedious calibration procedures. Our main st...

In this paper, learning algorithm for a multiplicative neural network motivated by spiking neuron model (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that the inclusion of a few more biological phenomena in the formulation of artificial neural network models make them more prevailing. Several bench...