##表格部分
###所用宏包
\usepackage{array} \usepackage{longtable} % 长表格自动分页,此宏包依赖array宏包 \usepackage{multirow} %不规则表格占用多行
合并表格
\begin{table}[h!] \centering \caption{Comparative results of coverage and specificity for synthetic 1-D numeric data with six clusters for selected values of $c$}\label{Tab:comp_TS_Granular_M} \begin{tabular}{ccccccccc} %\hline % after \\: \hline or \cline{col1-col2} \cline{col3-col4} ... %\\ %\hline \toprule \multirow{3}{*}{$c$} &\multicolumn{4}{c}{Standard granular TS model} & \multicolumn{4}{c}{Proposed granular fuzzy model} \\ \cline{2-9} &\multicolumn{2}{c}{Coverage} &\multicolumn{2}{c}{Specificity} &\multicolumn{2}{c}{Coverage} &\multicolumn{2}{c}{Specificity} \\ \cline{2-9} & Training& Testing& Training& Testing & Training& Testing& Training& Testing\\ \midrule 2 &0.5976 &0.5111 &0.4996 &0.4391 &0.1667 & 0.1611 &0.1579 &0.1504\\ 3 &0.5952 &0.5889 &0.5188 &0.4970 &0.2429 & 0.1444 &0.2133 &0.1242 \\ 4 &0.7214 &0.7667 &0.6374 &0.6161 &0.4738 & 0.4889 &0.4412 &0.4533 \\ 6 &0.7000 &0.7056 &0.6622 &0.6704 &0.6786 & 0.6556 &0.6403 &0.6172 \\ 8 &0.6167 &0.5556 &0.5907 &0.5357 &0.8000 & 0.8000 &0.7324 &0.7386 \\ 10 &0.5524 &0.5167 &0.5336 &0.5029 &0.8289 & 0.8167 &0.7707 &0.7673 \\ %12 &0.9357 &0.9389 &0.8751 &0.8791 &0.8190 & 0.9000 &0.7519 &0.8273 \\ \bottomrule \end{tabular} \end{table}
###长算法表格,跨页显示
\begin{longtable}{p{\linewidth}} \toprule \textbf{Algorithm 1} Pseudocode for constructing of input information granules based on FCM prototypes \\ \endfirsthead % Appear the table header at the top of every page \toprule \textbf{Algorithm 1} Pseudocode for constructing of input information granules based on FCM prototypes \\ \hline \endhead % Appear \hline at the bottom of every page \hline \endfoot \midrule \textbf{Input:} Numeric data collection $\bm D$, number of clusters $c$, values of $\alpha, \beta$, and division value of $\Delta \rho$, FCM termination criterion $\epsilon$.\\ \textbf{Output:} Input granules $A$. \\ \midrule 01. normalize $\bm D$ to $(0,1)$ \\ 02. generate a collection of numeric prototypes $\{ [\bm v, w]\}$ by using FCM \\ 03. $i\leftarrow 1$ \\ 04. \textbf{repeat} \\ 05. \qquad $j\leftarrow1$\\ 06. \qquad \textbf{repeat}\\ 07. \qquad\qquad $\rho_{ij} = j*\Delta\rho$\\ 08. \qquad\qquad Calculate ${\rm cov}(A_{ij})$ via \eqref{Eq:cov} \\ 09. \qquad\qquad Calculate ${\rm spec}(A_{ij})$ via \eqref{Eq:spec} \\ 10. \qquad\qquad Calculate $\sigma_{ij}$ via \eqref{Eq:diviation_y}\\ 11. \qquad\qquad Determine V($\rho_{ij}$) via \eqref{Eq:input_gran_radus_value} \\ 12. \qquad\qquad $j\leftarrow j+1$\\ 13. \qquad \textbf{until} $\rho_{ij}>1$\\ 14. \qquad Optimize $\rho_{i}$ by Max$( V(\rho_{ij}), j=1,2,\ldots,(1/\Delta\rho) )$\\ 15. \qquad \textbf{return} prototypes $\bm v_{i}$ and corresponding radii $\rho_{i}$\\ 16. \qquad $i \leftarrow i+1$\\ 17. \textbf{until} $i>c$\\ 18. \textbf{return} Input granules $\{ [\boldsymbol v_{1},\rho_{1}], [\boldsymbol v_{2},\rho_{2}],\ldots,[\boldsymbol v_{c},\rho_{c}] \}$, i.e., $\{ A_{1},A_{2},\ldots,A_{c} \}$\\ 19. \textbf{Check} overlaps between $A_{i}$s, \textbf{refresh} $\rho_{i}$s \\ \noindent \textbf{Output:} $A$ \\ \bottomrule \end{longtable}