Abstract
New Scaled Conjugate Gradient Algorithm for Training Artificial Neural Networks Based on Pure Conjugacy Condition
Highlights
Learning systems, such as multilayer feed-forward neural networks (FNN) are parallel computational models comprised of densely interconnected, adaptive processing units, characterized by an inherent propensity for learning from experience and discovering new knowledge
I 1 where net l j is the sum of the weight inputs for the j-th node in the l -th layer (j=1,2,..., Nl ), wi, j is the weights from the i-th neuron to the j-th neuron at the l 1, l th layer, respectively, b l j is the bias of the j-th neuron at the l-th layer and xlj is the output of the j-th neuron which belongs to the l -th layer, f ( ) is the activation function and
We will present experimental results in order to evaluate the performance of our proposed N1SCG in two problems the iris problem and continuous function approximation problem
Summary
Learning systems, such as multilayer feed-forward neural networks (FNN) are parallel computational models comprised of densely interconnected, adaptive processing units, characterized by an inherent propensity for learning from experience and discovering new knowledge. Since learning in realistic neural network applications often involves adjustment of several thousand weights only optimization methods that are applicable to large-scale problems, are relevant as alternative learning algorithms. Several conjugate gradient algorithms have recently been introduced as learning algorithms in neural networks [5,9,10].
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