Abstract
This paper concerns how to compute multi-valued functions using three-layer feedforward neural networks with one hidden layer. Firstly, we define strongly and weakly symmetric functions. Then we give a network to compute a specific strongly symmetric function. The number of the hidden neurons is given and the weights are 1 or -1. Algorithm 1 modifies the weights to real numbers to compute arbitrary strongly symmetric functions. Theorem 3 extends the results to compute any multi-valued functions. Finally, we compare the complexity of our network with that of binary one. Our network needs fewer neurons.
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© 2007 Springer Berlin Heidelberg
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Jiang, N., Yang, Y., Ma, X., Zhang, Z. (2007). Using Three Layer Neural Network to Compute Multi-valued Functions. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_1
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DOI: https://doi.org/10.1007/978-3-540-72395-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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