【求助】神经网络高手来。
			 
			 
			
		
		
		
			
			一运行程序就出现:“??? Error using ==> newff 
Input ranges has values in the second column larger in the values in the same row of the first column.”是什么意思? 
 
 
我的实验数据如下: 
结构:8-17-2 
 
 
 
 
                                     表1减速箱状态样本数据 
样本 
序号	样本输入特征数据	类别 
	x1	x2	x3	x4	x5	x6	x7	x8	 
1	-1.7817	-0.2786	-0.2954	-0.2394	-0.1842	-0.1572	-0.1584	-0.1998	1 
2	-1.8710	-0.2957	-0.3494	-0.2904	-0.1460	-0.1387	-0.1492	-0.2228	1 
3	-1.8347	-0.2817	-0.3566	-0.3476	-0.1820	-0.1435	-0.1778	-0.1849	1 
4	-1.8807	-0.2467	-0.2316	-0.2419	-0.1938	-0.2103	-0.2010	-0.2533	1 
5	-1.4151	-0.2282	-0.2124	-0.2147	-0.1271	-0.0680	-0.0872	-0.1684	2 
6	-1.2879	-0.2252	-0.2012	-0.1298	-0.0245	-0.0390	-0.0762	-0.1672	2 
7	-1.5239	-0.1970	-0.1094	-0.1402	-0.0994	-0.1394	-0.1673	-0.2810	2 
8	-1.6781	-0.2047	-0.1180	-0.1532	-0.1732	-0.1716	-0.1851	-0.2006	2 
9	0.1605	-0.0920	-0.0160	0.1246	0.1802	0.2087	0.2234	0.1003	3 
10	0.2045	0.1078	0.2246	0.2031	0.2428	0.2050	0.0704	0.0403	3 
11	-1.0242	-0.1461	-0.1018	-0.0778	-0.0363	-0.0476	-0.0160	-0.0253	3 
12	-0.7915	-0.1018	-0.0737	-0.0945	-0.0955	-0.0044	0.0467	0.0719	3 
 
                                               表2减速箱测试数据 
样本序 号	样本状态数据	类别 
	x1	x2	x3	x4	x5	x6	x7	x8	 
13	-1.4736	-0.2845	-3.0724	-0.2108	-0.1904	-0.1467	-0.1696	-0.2001	1 
14	-1.6002	-0.2011	-0.1021	-0.1394	-0.1001	-0.1572	-0.1584	-0.2790	2 
15	-1.0314	-0.1521	-0.1101  	-0.0801	-0.0347	-0.0482	-0.0158	-0.0301	3 
 
训练样本期望值 
 
0	    1	 
0	    1	 
0	    1	 
0       1 
1       0 
1       0 
1       0 
1       0 
1       1 
1       1 
1       1 
1       1 
 
 
测试样本期望值 
 
0	1	 
1   0 
 
1   1 
 
 
我编的程序如下: 
P=[-1.7817 -1.8710 -1.8347 -1.8807 -1.4151 -1.2879 -1.5239 -1.6781 0.1605 0.2045 -1.0242 -0.7915; 
   -0.2786 -0.2957 -0.2817 -0.2467 -0.2282 -0.2252 -0.1970 -0.2047 -0.0920 0.1078 -0.1461 -0.1018; 
   -0.2954 -0.3494 -0.3566 -0.2316 -0.2124 -0.2012 -0.1094 -0.1180 -0.0160 0.2246 -0.1018 -0.0737; 
   -0.2394 -0.2904 -0.3476 -0.2419 -0.2147 -0.1298 -0.1402 -0.1532 0.1246 0.2031 -0.0778 -0.0945; 
   -0.1842 -0.1460 -0.1820 -0.1938 -0.1271 -0.0245 -0.0994 -0.1732 0.1802 0.2428 -0.0363 -0.0955; 
   -0.1572 -0.1387 -0.1435 -0.2103 -0.0680 -0.0390 -0.1394 -0.1716 0.2087 0.2050 -0.0476 -0.0044; 
   -0.1584 -0.1492 -0.1778 -0.2010 -0.0872 -0.0762 -0.1673 -0.1851 0.2234 0.0704 -0.0160 0.0467; 
   -0.1998 -0.2228 -0.1849 -0.2533 -0.1684 -0.1672 -0.2810 -0.2006 0.1003 0.0403 -0.0253 0.0719]'; 
T=[0 1;0 1; 
   1 0;1 0; 
   1 1;1 1]'; 
threshold=[0 1;0 1;0 1;0 1;1 0;1 0;1 0;1 0;1 1;1 1;1 1;1 1]; 
net=newff(threshold,[17,2],{'tansig','logsig'},'trainlm'); 
net.trainParam.epochs=1000; 
net.trainParam.goal=0.01; 
LP.lr=0.1; 
net=train(net,P,T); 
P_test=[-1.4736 -1.6002 -1.0314; 
        -0.2845 -0.2011 -0.1521; 
        -3.0724 -0.1021 -0.1101;   
        -0.2108 -0.1394 -0.0801; 
        -0.1904 -0.1001 -0.0347; 
        -0.1467 -0.1572 -0.0482; 
        -0.1696 -0.1584 -0.0158; 
        -0.2001 -0.2790 -0.0301]'; 
Y=sim(net,P_test)
		 
		
		
		
		
		
		
		
	 |