Table 10 Complex picture fuzzy positive and negative ideal solutions.

From: TOPSIS driven complex picture fuzzy approach for speech matching and sports training feature recognition

 

\({A}^{+}\)

\({A}^{-}\)

\({m}_{{\text{M}}_{w}^{+}}^{r}\)

\({m}_{{\text{M}}_{w}^{+}}^{i}\)

\({a}_{{\text{M}}_{w}^{+}}^{r}\)

\({a}_{{\text{M}}_{w}^{+}}^{i}\)

\({n}_{{\text{M}}_{w}^{+}}^{r}\)

\({n}_{{\text{M}}_{w}^{+}}^{i}\)

\({m}_{{\text{M}}_{w}^{-}}^{r}\)

\({m}_{{\text{M}}_{w}^{-}}^{i}\)

\({a}_{{\text{M}}_{w}^{-}}^{r}\)

\({a}_{{\text{M}}_{w}^{-}}^{i}\)

\({n}_{{\text{M}}_{w}^{-}}^{r}\)

\({n}_{{\text{M}}_{w}^{-}}^{i}\)

\({C}_{1}\)

\(0.43\)

\(0.01\)

\(0.06\)

\(0.00\)

\(0.25\)

\(0.09\)

\(0.28\)

\(0.01\)

\(0.03\)

\(0.00\)

\(0.38\)

\(0.17\)

\({C}_{2}\)

\(0.53\)

\(0.01\)

\(0.04\)

\(0.00\)

\(0.20\)

\(0.08\)

\(0.33\)

\(0.01\)

\(0.03\)

\(0.00\)

\(0.34\)

\(0.16\)

\({C}_{3}\)

\(0.61\)

\(0.01\)

\(0.02\)

\(0.00\)

\(0.19\)

\(0.07\)

\(0.45\)

\(0.00\)

\(0.01\)

\(0.00\)

\(0.28\)

\(0.13\)

\({C}_{4}\)

\(0.46\)

\(0.01\)

\(0.04\)

\(0.00\)

\(0.22\)

\(0.08\)

\(0.33\)

\(0.00\)

\(0.02\)

\(0.00\)

\(0.34\)

\(0.11\)

\({C}_{5}\)

\(0.44\)

\(0.01\)

\(0.03\)

\(0.00\)

\(0.30\)

\(0.10\)

\(0.32\)

\(0.01\)

\(0.02\)

\(0.00\)

\(0.38\)

\(0.15\)

\({C}_{6}\)

\(0.37\)

\(0.01\)

\(0.04\)

\(0.00\)

\(0.32\)

\(0.09\)

\(0.24\)

\(0.01\)

\(0.02\)

\(0.00\)

\(0.44\)

\(0.17\)

\({C}_{7}\)

\(0.59\)

\(0.01\)

\(0.03\)

\(0.00\)

\(0.19\)

\(0.07\)

\(0.37\)

\(0.01\)

\(0.02\)

\(0.00\)

\(0.33\)

\(0.14\)

\({C}_{8}\)

\(0.28\)

\(0.01\)

\(0.02\)

\(0.00\)

\(0.37\)

\(0.18\)

\(0.46\)

\(0.01\)

\(0.04\)

\(0.00\)

\(0.25\)

\(0.11\)

\({C}_{9}\)

\(0.24\)

\(0.01\)

\(0.02\)

\(0.00\)

\(0.44\)

\(0.15\)

\(0.35\)

\(0.01\)

\(0.03\)

\(0.00\)

\(0.33\)

\(0.11\)

\({C}_{10}\)

\(0.30\)

\(0.01\)

\(0.03\)

\(0.00\)

\(0.36\)

\(0.16\)

\(0.42\)

\(0.01\)

\(0.05\)

\(0.00\)

\(0.28\)

\(0.11\)