## 5 Experimental Results
### Determination of the Number of Neurons in the Hidden Layers  
The _newelm_() function in Matlab is used to create the Elman neural network, and its expression is as follows:  
\[net=newelm(P,T,[S_{1},S_{2},\dots,S_{i}],\{TF_{1},TF_{2},\dots,TF_{n}\},BTF,LF, PF) \tag{27}\]  
Among them, tansig is selected as the activation function because its convergence speed is faster than that of the commonly used sigmoid function, and the number of iterations can be reduced. The training function has a great influence on neural network training. We use the default trainlm as the training function. Compared with the trainbfg and trainingd, although it occupies more memory resources, it has a faster computing speed. The specific parameter settings are shown in the Table 3.  
In order to design a neural network model that is more suitable for enterprise collaboration performance evaluation, experiments were carried out to compare networks with different numbers of neurons in the hidden layer. The experimental comparison results are shown in Table 4 and Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(\mathbf{E_{1}\text{ to }E_{6}}\) & \(\mathbf{E_{1}\text{ to }E_{9}}\) & \(\mathbf{E_{1}\text{ to }E_{11}}\) & \(\mathbf{E_{2}\text{ to }E_{3}}\) & \(\mathbf{E_{2}\text{ to }E_{5}}\) & \(\mathbf{E_{3}\text{ to }E_{9}}\) & \(\dots\) & \(\mathbf{E_{25}\text{ to }E_{15}}\) \\ \hline \(G_{1}\) & 0.4988 & 0.0100 & 0.4214 & 0.8787 & 0.9532 & 0.5252 & \(\dots\) & 1.0000 \\ \(G_{2}\) & 1.0000 & 0.6694 & 0.0100 & 0.7963 & 0.4686 & 0.2982 & \(\dots\) & 0.9151 \\ \(G_{3}\) & 0.7722 & 0.8716 & 0.5572 & 1.0000 & 0.5973 & 0.9035 & \(\dots\) & 0.9694 \\ \(G_{4}\) & 0.1829 & 0.9707 & 0.8469 & 0.2571 & 0.0100 & 0.8453 & \(\dots\) & 0.5112 \\ \(G_{5}\) & 0.9751 & 0.7493 & 0.4631 & 0.7991 & 0.1266 & 0.4948 & \(\dots\) & 0.5849 \\ \(G_{6}\) & 0.3793 & 0.6735 & 0.4810 & 0.9054 & 0.7606 & 1.0000 & \(\dots\) & 0.6656 \\ \(G_{7}\) & 0.6774 & 0.4573 & 0.7871 & 0.3023 & 0.4479 & 0.2243 & \(\dots\) & 0.0100 \\ \(G_{8}\) & 0.5226 & 0.0100 & 1.0000 & 0.4707 & 0.3411 & 0.8328 & \(\dots\) & 0.1339 \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ \(G_{21}\) & 0.6381 & 0.5744 & 0.3273 & 0.3561 & 0.8465 & 0.3570 & \(\dots\) & 0.0100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: 50 pieces of collaborative data from 25 collaborative enterprises after normalization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Meaning** & **Set Value** \\ \hline P & The value range of the input matrix & input(;1:40) \\ T & The value range of the output matrix & output(;1:40) \\ TF\({}_{i}\) & The transfer function of the i-th layer & trains \\ BTF & The training function & trainlm \\ epochs & Number of iterations over all training samples & 500 \\ goal & Training accuracy & \(1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The meaning and setting of experimental parameters.  
Figure 2: The collaborative relationship between enterprises.  
As can be seen from Table 4 and Figure 3, when the number of neurons in the hidden layer increases from 6 to 11, the error of the model shows a decreasing trend as a whole. Until the number of neurons in the hidden layer increases to 11, the model has the lowest error of 0.0009990843. After that, the error of the model will continue to increase as the number of neurons in the hidden layer increases. Therefore, in the subsequent experiments, the number of neurons in the hidden layer of the Elman neural network is 11.