Constructive Neural Networks: a Review

Constructive algorithms for structure learning in feedforward neural networks for regression problems

In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole problem as a state-space search, we first describe the general issues in constructive algorithms...

On Existential and Constructive Neural Complexity Results

In this paper we will review major circuit complexity results for networks of perceptrons. In the first part we will present many theoretical results, while the second part is much more practical, as comparing nine different constructive solutions for a particular but important case: the addition of two binary numbers.

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

Estimation of Industrial Production Costs, Using Regression Analysis, Neural Networks or Hybrid Neural - Regression Method?

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...

Pii: S0893-6080(97)00097-x

In this paper, we present a review of some recent works on approximation by feedforward neural networks. A particular emphasis is placed on the computational aspects of the problem, i.e. we discuss the possibility of realizing a feedforward neural network which achieves a prescribed degree of accuracy of approximation, and the determination of the number of hidden layer neurons required to achi...