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On the basis of literature 6, cell status samples are established with the frequency
energy as the characteristic vectors. The input characteristic vectors are P0, P1, P2, P3,
P4, P5, P6, P7, P8, P9. They represent the total energy with the frequency respectively
between
0~0.01H
z
0.01~0.02Hz
0.02~0.03H
z0.03~0.04Hz0.04~0.05Hz
0.05~0.06Hz0.06~0.07Hz0.07~0.08Hz and 0.08~0.09Hz.
We take three kinds of cell status as the network output characteristic vectors. The
following forms can represent them.
Y
1:1 0 0 donates the cell status is normal;
Y2
:
(0 1 0) donates the liquid aluminum is fluctuating;
Y
3:0 0 1 donates the anode is
abnormal.
Table 1.
Characteristic of aluminum production cells under the diffident status
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3.2 Determining the Nodes
Neurons at the input layer are determined by the dimensions of input characteristic
vectors. By referring to the discussion above, the dimensions are 10. That is to say the
nodes are also 10. Neural nodes at the output layer are determined by the different kinds
of status. In this paper we mainly talk about three circumstances. As a result, the nodes
of output layer are 3. According to the rule of determining the hidden nodes under 3
layers of BP neural network, taking the number of hidden nodes for 8. After repeated
experiments, the learning rate was selected as 0.08 and expectative error as 0.005.
3.3 Establishing the Neural Network Model
According to the analysis above, we establish a BP neural network model with 10 input
neurons, 3 output neurons and 8 hidden neurons. Here we assume that the BP neural
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