This paper presents a message-passing architecture simulating multilayer neural networks, adjusting its weights for each pair, consisting of an input vector and a desired output vector. First, the multilayer neural network is defined, and the difficulties arising from parallel implementation are cla
Three algorithms for estimating the domain of validity of feedforward neural networks
β Scribed by Pierre Courrieu
- Publisher
- Elsevier Science
- Year
- 1994
- Tongue
- English
- Weight
- 527 KB
- Volume
- 7
- Category
- Article
- ISSN
- 0893-6080
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The number of required hidden units is statistically estimated for feedforward neural networks that are constructed by adding hidden units one by one. The output error decreases with the number of hidden units by an almost constant rate, if each appropriate hidden unit is selected out of a great num
We propose in this paper a novel prescriptive solution to decide the optimum number of neurons in the hidden-layer of multilayer feedforward neural networks. Our approach uses the unconstrained mixed integer nonlinear multicriteria optimization technique. We validate the algorithm using numerical ex