Interpretation Trained Neural Networks Based on Genetic Algorithms

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Provided by: Academy & Industry Research Collaboration Center
Topic: Networking
Format: PDF
In this paper, constructive learning is used to train the neural networks. The results of neural networks are obtained but its result is not in comprehensible form or in a black box form. The authors' goal is to use an important and desirable model to identify sets of input variable which results in a desired output value. The nature of this model can help to find an optimal set of difficult input variables. Accuracy genetic algorithms are used as an interpretation of achieving neural network inversion.
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