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It is important to study the neural network (NN) when it falls into chaos, because brain dynamics involve chaos. In this paper, the several chaotic behaviors of supervised neural networks using Hurst Exponent (H), fractal dimension (FD) and bifurcation diagram are studied. The update rule for NN trained with back-propagation (BP) algorithm absorbs the function of the form x(1-x) which is responsible...
This paper presents a complex-valued neuron (CVN) model for real-valued classification problems incorporating a new activation function. The activation function maps complex-valued net-inputs (sum of weighted inputs) of a neuron into bounded real-values, and its role is to divide the net-input space into different regions for different classes. A gradient-descent learning rule has been derived to...
We propose a training algorithm for multi-layered neural-network (NN) classifiers that improves the generalization ability, the ability to classify previously unseen patterns. A correlation term was added to the error function of the standard back-propagation (BP) learning algorithm, and a new weight update rule was derived. The correlation term was to make hidden node activations positively correlated,...
This paper presents a neural network ensemble creation method where component networks are determined automatically by sequential training. Only previously misclassified patterns by existing component networks are used for training coming component networks sequentially. This training strategy forces coming component networks to work only on the unsolved portion of the input space. As a result coming...
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