The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

We study the number of hidden layers required by a multilayer neu (cid: 173) ral network
with threshold units to compute a function f from nd to {O, I}. In dimension d= 2, Gibson …

This section considers some of the alternative approaches towards modeling biological
functions by digital circuits. It starts by introducing some circuit complexity issues and …

Identifies the linear independence of the internal representation of the multilayer perceptron
as an essential property for exact learning. The sigmoidal hidden unit activation function has …

In this survey paper, the-state-of-the-art of the optimal structure design of Multilayer
Feedforward Neural Network (MFNN) for pattern recognition is reviewed. Special emphasis …

An algorithm is proposed for training the single-layered perceptron. The algorithm follows
successive steepest descent directions with respect to the perceptron cost function, taking …

This paper considers the classification properties of two-layer networks of McCulloch–Pitts
units from a theoretical point of view. In particular we consider their ability to realise exactly …

This paper presents observations of plasma blobs by nightglow OI 630.0 nm emissions
using ground-based techniques, all sky imager and photometer from Kolhapur. The …

This paper considers the classification capabilities of neural networks which incorporate a
single hidden layer of McCulloch-Pitts units. Previous research by the authors has led to a …

We study the number of hidden layers required by a multilayer neural network with threshold
units to compute a dichotomy from ℝ d to {0, 1}, defined by a finite set of hyperplanes. We …