Abstract
Our recent works on artificial neural networks point to the possibility of extending the activation function of a standard artificial neuron model using the conditional signal accumulation technique, thus significantly enhancing the capabilities of neural networks. We present a new artificial neuron model, called Sigma-if, with the ability to dynamically tune the size of the decision space under consideration, resulting from a novel activation function. The paper discusses construction of the proposed neuron as well as training Sigma-if feedforward neural networks for well known sample classification problems.
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Huk, M., Kwasnicka, H. (2005). The Concept and Properties of Sigma-if Neural Network. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_4
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DOI: https://doi.org/10.1007/3-211-27389-1_4
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-24934-5
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