chenggongdu.m

改进的神经网络PSO,主要用于成功度评价,本例是电厂的成功度评价模型和数据.

p=[0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
0.0357	0.0214	0.0543	0.0429	0.0357	0.0543	0.0543	0.0357	0.0357	0.06	0.0357	0.0557	0.0914	0.1143	0.1014	0.0714	0.0657	0.0471	0.0471	0.0471	0.0557
0.0714	0.0429	0.1086	0.0857	0.0714	0.1086	0.1086	0.0714	0.0714	0.12	0.0714	0.1114	0.1829	0.2286	0.2029	0.1429	0.1314	0.0943	0.0943	0.0943	0.1114
0.1429	0.0857	0.2171	0.1714	0.1429	0.2171	0.2171	0.1429	0.1429	0.24	0.1429	0.2229	0.3657	0.4571	0.4057	0.2857	0.2629	0.1886	0.1886	0.1886	0.22286
0.1786	0.1071	0.2714	0.2143	0.1786	0.2714	0.2714	0.1786	0.1786	0.3	0.1786	0.2786	0.4571	0.5714	0.5071	0.3571	0.3286	0.2357	0.2357	0.2357	0.2786
0.2143	0.1286	0.3257	0.2571	0.21429	0.3257	0.3257	0.2142	0.2142	0.36	0.2142	0.3342	0.5486	0.6857	0.6086	0.4286	0.3943	0.2829	0.2829	0.2829	0.3343
0.25	0.15	0.38	0.3	0.25	0.38	0.38	0.25	0.25	0.42	0.25	0.39	0.64	0.8	0.71	0.5	0.46	0.33	0.33	0.33	0.39
0.3043	0.21	0.4157	0.3286	0.3043	0.4157	0.4157	0.3043	0.3043	0.5014	0.3043	0.4757	0.69	0.8271	0.75	0.57	0.5357	0.4243	0.3771	0.3771	0.4757
0.3586	0.27	0.4514	0.3571	0.3586	0.4514	0.4514	0.3586	0.3586	0.5829	0.3586	0.5614	0.74	0.8543	0.79	0.64	0.6114	0.5186	0.4243	0.4243	0.5614
0.4671	0.39	0.5229	0.4143	0.4671	0.5229	0.5229	0.4671	0.4671	0.7457	0.4671	0.7329	0.84	0.9086	0.87	0.78	0.7629	0.7071	0.5186	0.5186	0.7329
0.5214	0.45	0.5586	0.4429	0.5214	0.5586	0.5586	0.5214	0.5214	0.8271	0.5214	0.8186	0.89	0.9357	0.91	0.85	0.8386	0.8014	0.5657	0.5657	0.8186
0.5757	0.51	0.5943	0.4714	0.5757	0.5943	0.5943	0.5757	0.5757	0.9086	0.5757	0.9043	0.94	0.9629	0.95	0.92	0.9143	0.8957	0.6129	0.6129	0.9043
0.63	0.57	0.63	0.5	0.63	0.63	0.63	0.63	0.63	0.99	0.63	0.99	0.99	0.99	0.99	0.99	0.99	0.99	0.66	0.66	0.99
0.7357	0.6929	0.7357	0.6429	0.7357	0.7357	0.7357	0.7357	0.7357	0.9929	0.7357	0.9929	0.9929	0.9929	0.9929	0.9929	0.9929	0.9929	0.7571	0.7571	0.9929
0.7886	0.7543	0.7886	0.7143	0.7886	0.7886	0.7886	0.7886	0.7886	0.9943	0.7886	0.9943	0.9943	0.9943	0.9943	0.9943	0.9943	0.9943	0.8057	0.8057	0.9943
0.8414	0.8157	0.8414	0.7857	0.8414	0.8414	0.8414	0.8414	0.8414	0.9957	0.8414	0.9957	0.9957	0.9957	0.9957	0.9957	0.9957	0.9957	0.8543	0.8543	0.9957
0.9473	0.9386	0.9471	0.9286	0.9471	0.9471	0.9471	0.9471	0.9471	0.9986	0.9471	0.9986	0.9986	0.9986	0.9986	0.9986	0.9986	0.9986	0.9514	0.9514	0.9986
1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
]';
t=[0.1 0.11429 0.12857 0.15714 0.17143 0.18571 0.2 0.21429 0.22857 0.25714 0.27143 0.28571 0.3 0.32857 0.34286 0.35714 0.38571 0.4];
threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1];
net=newff(threshold,[10,1],{'tansig','logsig'},'trainlm')
net.trainParam.epochs=1000;
net.trainParam.goal=0.00001;
LP.lr=0.00001;
net=train(net,p,t);
p_test=[0.1071	0.0643	0.1629	0.1286	0.1071	0.1629	0.1629	0.1071	0.1071	0.18	0.1071	0.1671	0.2743	0.3429	0.3043	0.2143	0.1975	0.1415	0.1414	0.1414	0.1671
0.4129	0.33	0.4871	0.3857	0.4129	0.4871	0.4871	0.4129	0.4129	0.6643	0.4129	0.6471	0.79	0.8814	0.83	0.71	0.6871	0.6129	0.4714	0.4714	0.6471
0.6829	0.6314	0.6829	0.5714	0.6829	0.6829	0.6829	0.6829	0.6829	0.9914	0.6829	0.9914	0.9914	0.9914	0.9914	0.9914	0.9914	0.9914	0.7086	0.7086	0.9914
0.8943	0.8771	0.8943	0.8571	0.8943	0.8943	0.8943	0.8943	0.8943	0.9971	0.8943	0.9971	0.9971	0.9971	0.9971	0.9971	0.9971	0.9971	0.9029	0.9029	0.9971
]';
y=sim(net,p_test)