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- W1551520385 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." @default.
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- W1551520385 date "2007-07-16" @default.
- W1551520385 modified "2023-10-16" @default.
- W1551520385 title "Using Three Layer Neural Network to Compute Multi-valued Functions" @default.
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- W1551520385 doi "https://doi.org/10.1007/978-3-540-72395-0_1" @default.
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