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前向人工神經網絡敏感性研究-資料下載頁

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【正文】 ] [ ] bias= 3 (Obtained by removing the 5th neuron from the MLP of 251) 13253 [ ] [ ] [ ] [ ] [ ] bias= 36 ? Simulation 2 (Classification) – Implement an MLP to solve the XOR problem: 0 1 ? Implementation considerations – The MLP architecture is restricted to 2n1. – The convergence condition is MESgoal=amp。Epoch?105. – The pruning processes start with MLPs of 251 and stop at an architecture of 241. – The relevant data used by and resulted from the pruning process are listed in Table 3 and Table 4. ?),( 21 xxFamp。amp。 2121 ???????? xxorxxamp。amp。 2121 ???????? xxorxx37 TABLE 3. Data for 3 MLPs with 5 hidden neurons to realize the function MLP 251 Epoch MSE (training) MSE (testing) Trained weights and bias (goal= amp。 epoch=100000) Sensitivity Relevance 1 44518 [ ] [ ] [ ] [ ] [ ] [ ] bias=0 2 51098 [ ] [ ] [ ] [ ] [ ] [ ] bias=0 3 33631 [ ] [ ] [ ] [ ] [ ] [ ] bias=0 38 TABLE 4. Data for the 3 pruned MLPs with 4 hidden neurons to realize the function MLP 241 Epoch MSE (training) MSE (testing) Retrained weights and bias (goal= amp。 epoch=100000) Sensitivity Relevance 1 (Obtained by removing the 4th neuron from the MLP of 251) 22611 [ ] [ ] [ ] [ ] [ ] bias= 2 (Obtained by removing the 2nd neuron from the MLP of 251) 14457 [ ] [ ] [ ] [ ] [ ] bias= 3 (Obtained by removing the 3rd neuron from the MLP of 251) 17501 [ ] [ ] [ ] [ ] [ ] bias=

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