3 research outputs found
Robust Design of Artificial Neural Networks Methodology in Neutron Spectrometry
Applications of artificial neural networks (ANNs) have been reported in literature in various
areas. [1ā5] The wide use of ANNs is due to their robustness, fault tolerant and the ability
to learn and generalize, through training process, from examples, complex nonlinear and
multi input/output relationships between process parameters using the process data. [6ā10]
The ANNs have many other advantageous characteristics, which include: generalization,
adaptation, universal function approximation, parallel data processing, robustness, etc.
Multilayer perceptron (MLP) trained with backpropagation (BP) algorithm is the most used
ANN in modeling, optimization classification and prediction processes. [11, 12] Although
BP algorithm has proved to be efficient, its convergence tends to be very slow, and there is a
possibility to get trapped in some undesired local minimum. [4, 10, 11, 13]
Most literature related to ANNs focused on specific applications and their results rather
than the methodology of developing and training the networks. In general, the quality
of the developed ANN is highly dependable not only on ANN training algorithm and its
parameters but also on many ANN architectural parameters such as the number of hidden
layers and nodes per layer which have to be set during training process and these settings
are very crucial to the accuracy of ANN model. [8, 14ā19
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