Studies on some properties of a mapping neural network

Ying Lin Yu; Wei Zhang

doi:10.1117/12.152538

2 September 1993 Studies on some properties of a mapping neural network

Ying Lin Yu, Wei Zhang

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152538
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

How to determine the element numbers in hidden Layers is a key problem in architecture design of a multi-layer ANN (Artificial Neural Network). In order to solve this problem we propose a growth model of elements in hidden layer. In a multi-layer ANN, (for example three layers) the element numbers in input and output layer are obviously determined by the requirements of a given problem, but the problem of how to determine the element numbers in hidden layer is left with a certain randomness. It is sure that the more complex the problem, the higher the degree of nonlinearity, the higher the accuracy is required, and the larger amounts of hidden layer elements are needed. The approach adopted to this problem is briefly described as follows. we first use a fewer hidden layer elements, and check whether this amounts meet the requirements of the given problem complexity, if this fails to meet it, a new element can be grown out. Working in this way until given requirements can be fmnaly satisfied, we get an ANN architecture with a properly determined hidden layer element numbers. The neccessary steps are as follows. (1). Determine the element numbers both in input and output layer according to the given requirements, select an initial amount of the hidden layer elements and form an initial ANN. (2). Set a given training accuracy E , and set a maximun value K of the grown elements in hidden layer. (3). Train the initial ANN by a faster BP algorithm, and get a fmnal error e. (4). If Ie < E , stop. (5). Record the Value L, which denotes the times that Ie < , if L <K, then return to step (3) to get off the occasional errors. (6). If L <K, add one or more new elements to the initial hidden layer, based on these all trained parameters, go back to step (3). Finnaly, we get a grown stable ANN architecture. Computer simulation results are shown in Tab.1

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Ying Lin Yu and Wei Zhang "Studies on some properties of a mapping neural network", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152538

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KEYWORDS

Fractal analysis

Artificial neural networks

Neural networks

Computer simulations

Associative arrays

Lithium

Detection and tracking algorithms

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