Abstract
Gelenbe has modeled neural networks using an analogy with queuing theory. This model (called Random Neural Network) calculates the probability of activation of the neurons in the network. Recently, Fourneau and Gelenbe have proposed an extension of this model, called multiple classes random neural network model. The purpose of this paper is to describe the use of the multiple classes random neural network model to learn patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon non-linear equations of the multiple classes random neural network model using gradient descent of a quadratic error function. In addition, we propose a progressive retrieval process with adaptive threshold values. The experimental evaluation shows that the learning algorithm provides good results.
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Aguilar, J. Learning Algorithm and Retrieval Process for the Multiple Classes Random Neural Network Model. Neural Processing Letters 13, 81–91 (2001). https://doi.org/10.1023/A:1009611918681
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DOI: https://doi.org/10.1023/A:1009611918681