平成24年度 電気関係学会四国支部連合大会

171-17

Copy Weight Parametersfor Back Propagation
Naohiro SHIBUYA, Yuta YOKOYAMA, Tomoya SHIMA,
Chihiro IKUTA, Yoko UWATE and Yoshifumi NISHIO(Tokushima University)

1. Introduction
The human brain is classified into the cerebrum, cerebellum, and brain stem. The brain is able to process thevast amounts of information, because the neurons makethe huge and the complex network. The cerebellum havethe function of the copy thinking of the cerebrum. We
focus attention on the function of the cerebrum and thecerebellum.
In this study, we apply such function to the Back Propagation (BP) of artificial neural network. We consider thehidden layer with two groups. The connection weight parameter of the neurons in the one group are copied into
the neurons in the other group.

2. Copy thinking of the cerebrum
We consider feed-forward neural network with three layers and the hidden layer is assorted to two groups. We
assume that the one group is the cerebrum group andthe other group is the cerebellum group. First, the neurons learn by the cerebrum group in the hidden layer andupdate the connection weight. Second, the connection
weight parameter of the neurons in the cerebrum groupare copied into the cerebellum group. Finally, the neurons learn by the cerebellum group in the hidden layer
and update the connection weight. The proposed network
is shown in Fig. 1.

w is the connection weight. The square error used for error
evaluation is described by Eq. (4).
1∑(ti − Oi )2 ,
2
n

E=(4)

i=1

where E is the error, n is the number of output, t is thetarget value and O is the output value.

3. Simulation Result
In this study, we consider that the number of neurons
in the hidden layer is 12. The number of neurons in thecerebrum group are set to 4. The number of neurons inthe cerebellum group are set to 8. The learning time is20000. At the 4000 learning time, the connection weightparameter of the neurons in the cerebrum group are copied
into the neurons in the cerebellum group. Finally, theneurons in the hidden layer learn by the neurons of thecerebellum group.
We apply the function approximation to the proposednetwork. We consider the two-dimensional of Chebyshev
polynomial. We compare the proposed network with theconventional network. Figure 2 shows the learning curvesof the conventional network and the proposed network.

Figure 2: Simulation result.

Figure 1: Composition of the proposed network.
The output function is described by Eq. (1). Moreover,the internal state and sigmoid function are described byEqs. (2) and (3).
xi (t + 1) = f (ui (t + 1)) ,(1)ui (t + 1)=

∑wij xj (t),(2)jf (a)=1,
1 + e−a(3)where x is the input or output, u is the internal state andPowered by TCPDF (www.tcpdf.org)

From Fig. 2, we can see that the error of the conventional network is the worst. The learning performanceof the proposed network is better than the conventional
network. Because we consider that the connection weightparameter of the neurons in the cerebrum group are copied
into the neurons in the cerebellum group.

4. Conclusions
In this study, we proposed the connection weight parameter of the neurons in the cerebrum group copied into
the neuron in the cerebellum group. First, the neurons
learned by the cerebrum group in the hidden layer. Next,
the neurons learned by the cerebellum group in the hidden
layer. The learning performance of the proposed network
is better than the conventional network.