Improved Adaptive Learning Algorithm forConstructive Neural Networks

Abstract

Abstract-Constructive Neural Network learning algorithms provide incremental ways to determine the near-minimal architecture of a multi layer perceptron network along with learning algorithms for determining appropriate weights for pattern classification problems. An improved version of adaptive learning algorithm in a structured multilayer networks is proposed in this research work. A proper weight setting for the constructive architecture to solve pattern classification problems is analyzed and tabulated.

Optimization of multi-layer artificial neural networks using delta values of hidden layers

“…Sridar et al [11] improved the adaptive learning algorithm for multi-category tiling constructive neural networks for pattern classification problems.…”

“…Moniral et al have proposed a new adaptive merging and growing algorithm to design artificial neural networks in [10]. Further, Sridhar in [11] proposed an improved version of adaptive learning algorithm in a structured multilayer networks. In addition, some diverse algorithms in constructive neural networks have been presented by [16] .…”