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从函数图像上看有最小值，但是FindMinimum运行出来没有结果 Mw = 150; trainwstd = {-1.72556, 0.180119, -0.0761951, -0.919702, -0.645379, -0.562186, 0.0237736, -1.17307, -0.786781, 0.640184, -1.13842, -0.347712, 0.552852, -1.70327, -1.32033, -0.424672, -0.00567068, 1.25186, 0.672737, -0.941094, 1.0882, -0.981721, -1.30887, -1.39805, -0.706932, 1.18618, -1.41693, 1.06552, -0.276525, -0.502298, 0.330531, -0.365598, 1.24788, -0.559414, -0.391273, 0.773954, -0.00637404, 1.0452, 0.660235, -0.875976, -1.46725, 0.406424, 0.319978, 0.538741, 0.358374, 0.822705, 1.6833, -0.0429328, 1.16274, -1.64419, -0.331392, -1.55187, 0.687817, 0.89276, 1.44877, 0.503764, -1.25926, -0.532904, -0.929284, 1.38899, 1.23069, 1.44418, -1.6547, -0.714061, -0.933805, -0.589449, -1.6463, 1.18151, 0.149355, -0.732406, -1.25421, -1.04845, 0.829899, 0.725189, 0.735345, -0.748387, 0.391668, 0.37947, 1.30034, -0.653533, -1.14318, -1.68717, -0.394622, -1.07531, 0.258877, 1.33309, -0.889856, -1.72848, 1.5881, 0.440526, 1.65083, 1.28694, -0.498147, 0.259437, -0.747129, 0.875868, 1.63336, 1.6343, -0.223355, 0.979203, -0.085668, -1.69569, 1.5494, 0.913691, 1.42544, -0.67527, -1.14655, -0.880803, 0.730562, -0.994624, 0.504969, -0.608228, 1.30111, 1.47652, 1.0402, 0.526138, -0.407679, -0.249245, 1.41271, 0.638975, -1.21881, -1.01831, -1.1003, 1.10972, 0.925814, 1.14621, 0.346299, -1.32344, 1.55579, -0.226157, -1.70774, 0.701837, -0.538662, -0.283466, 1.63141, -1.01584, -0.703272, -0.585422, 0.859255, 0.262687, 0.573587, 0.745942, 0.775049, -0.459221, -1.1851, 0.242997, 0.23075, 1.04684, -1.2971, 1.17258}； trainLastd={1.80785, -1.48019, 1.21707, -0.770369, 1.3838, 1.84627, -0.66789, 0.373994, 1.13384, -0.946803, 0.0036877, -0.561397, -0.894387, 1.34242, 1.48309, 0.415332, -1.04708, -1.60584, -0.110102, -1.1804, -2.32267, -0.224717, -1.24382, 1.0107, 1.03249, -2.31672, 0.262735, 0.0298984, 1.4535, 1.35633, 0.890072, 0.993755, -1.48913, 1.60578, 1.25612, -0.891795, -1.24602, -0.947351, 1.05654, 0.209499, 1.42942, 0.736553, -0.753368, -1.08727, 1.64438, -1.43615, 0.369108, 0.907761, 0.364607, -0.0722066, 0.574003, -0.473916, 1.17604, -0.787614, 0.632069, -0.693557, -0.731288, -0.314412, 0.866853, -1.13619, -1.00356, -1.16811, -0.851511, 0.437804, 0.94492, 0.478376, 1.09586, -1.82818, 0.125129, 0.860987, 0.24783, -1.65594, -0.315481, -0.716075, 1.03175, 1.23085, 0.417587, 0.580768, 0.30178, 0.059187, 0.752655, 0.360162, 0.77839, 0.714254, 0.0466191, -1.43666, 0.852508, 1.73625, -1.41331, 0.952687, 0.579627, 0.0499512, -0.0049781, 0.849055, 0.375547, 0.956581, -0.203829, -1.51415, 0.172607, -0.970064, 0.731311, 1.14594, -1.19221, -0.737915, -0.193791, 0.334946, 0.809549, -1.20778, -1.1728, -0.204631, 0.582626, 0.0198714, -1.10626, 0.694766, -1.34239, 0.980495, 0.571377, -1.26083, 0.119862, 0.826546, -0.409742, 0.862276, 0.807611, 0.108383, -1.27024, -2.38063, 1.19822, 0.544958, -1.02215, 0.306651, 0.305285, 1.46954, -0.233644, 0.0212571, -1.9115, -0.464432, -0.82893, 1.146, -1.21004, 0.379003, 0.255409, -0.417888, -0.970193, 1.09538, -1.34996, -0.719621, 0.648206, -0.434638, -1.28144, 0.0173775}； Alpha = Table[b1, {Mw}]; result4 = Table[Norm[trainwstd[[i]] - trainwstd[[j]]], {i, 1, Mw}, {j, 1, Mw}]; result5 = Exp[-(result4^2)*Alpha*0.5]; f = Table[Sum[result5[[i, j]], {j, 1, Mw}], {i, 1, Mw}]; ff = Table[D[f[[i]], b1], {i, 1, Mw}]; Alpha1 = Table[b1 + (f[[1]] - f[[j]])/ff[[j]], {j, 2, Mw}]; result0 = Table[Norm[trainwstd[[j]] - trainwstd[[i]]], {j, 2, Mw}, {i, 1, j - 1}]; result11 = Table[trainLastd[[i]]* Exp[-(result0[[j - 1, i]]^2)*Alpha1[[j - 1]]*0.5], {j, 2, Mw}, {i,1, j - 1}]; result1 = PadRight[result11]; result22 = Table[Exp[-(result0[[j - 1, i]]^2)*Alpha1[[j - 1]]*0.5], {j, 2, Mw}, {i, 1, j - 1}]; result2 = PadRight[result22]; predtrainLastdup = Table[Sum[result1[[i, j]], {j, 1, Mw - 1}], {i, 1, Mw - 1}]; predtrainLastddown = Table[Sum[result2[[i, j]], {j, 1, Mw - 1}], {i, 1, Mw - 1}]; predtrainLastd = predtrainLastdup/predtrainLastddown; result33 = Table[Exp[-2*(result0[[j - 1, i]]^2)], {j, 2, Mw}, {i, 1, j - 1}]; result3 = PadRight[result33]; rou = Table[Sum[result3[[i, j]], {j, 1, Mw - 1}], {i, 1, Mw - 1}]; matrixb2 =Table[rou[[i]]*(trainLastd[[i + 1]] - predtrainLastd[[i]])^2, {i, 1, Mw - 1}]; b2 = Sum[matrixb2[[i]]/(Mw - 1), {i, 1, Mw - 1}]; postp = -Log[b2^(-(Mw - 1)/2)]*Exp[-(Mw - 1)/2]; Plot[postp, {b1, 0, 50}]optb1 = FindMinimum[{postp, 0.0001 < b1 < 50}, b1] 运行最后一行代码，没有结果，软件“叮咚”一声，就没有然后了，有没有大佬知道这是什么原因呀？

利用RecurrenceTable生成数据结果正确，但是程序会报错 Nw = 300; (*w1生成*) ndist1 = UniformDistribution[{-2, 5}]; w1 = Table[RandomReal[ndist1], {Nw}]; (*e生成*) ndist2 = NormalDistribution[0, 0.3]; e = Table[RandomReal[ndist1], {Nw}]; ndist3 = NormalDistribution[]; (*w2生成*) e2 = Table[RandomReal[ndist3], {Nw}]; w21 = 0.05 + e2[[1]]; w22 = 0.05 + 0.7*w21 + e2[[2]]; w23 = 0.05 + 0.7*w22 + 0.15*w21 + e2[[3]]; w2 = RecurrenceTable[{w2[n] == 0.05 + 0.7*w2[n - 1] + 0.15*w2[n - 2] - 0.1*w2[n - 3] + e2[[n]], w2[1] == w21, w2[2] == w22, w2[3] == w23}, w2, {n, 1, 300}]; (*w23生成*) e3 = Table[RandomReal[ndist3], {Nw}]; w31 = 0.02 + e3[[1]]; w32 = 0.02 + 0.6*w31 + e3[[2]]; w33 = 0.02 + 0.6*w32 + 0.35*w31 + e3[[3]]; w34 = 0.02 + 0.6*w33 + 0.35*w32 - 0.1*w31 + e3[[4]]; w3 = RecurrenceTable[{w3[n] == 0.02 + 0.6 w3[n - 1] + 0.35 w3[n - 2] - 0.1 w3[n - 3] - 0.3 w3[n - 4] + e3[[n]], w3[1] == w31, w3[2] == w32, w3[3] == w33, w3[4] == w34}, w3, {n, 1, 300}]; (*La生成*) La = Sin[3 w1] + 2 Cos[0.4 w1] + e; 这段代码总是会出现下面的报错：但是结果时正确的。可是这些报错的内容占很大篇幅，每次都要去删，有大佬知道怎么解决这个问题么？新手菜鸟，救救孩子

根据函数图判断的最小值，不是用FindMinimum求出的最小值有没有大佬可以帮忙看下我的代码，FindMinimum求出局部最小值，而不是指定范围内的最小值，这种情况应该怎么解决呀？ Mw = 30; trainwstd={-0.343619, -1.78989, -0.380043, 1.72798, -0.627254, 0.348522, 0.656261, -0.768291, -0.503788, -0.145976, 1.07402, 1.75997, -0.270296, -0.558593, -1.77163, 0.572725, -0.156404, -1.67037, -0.506315, -0.462523, 1.05856, 1.32859, 0.959541, 1.44407, 0.25478, -0.0461008, -0.648199, -1.45474, 0.924093, -0.00507966}; trainLastd={0.816689, -0.060828, -0.328293, -1.54609, 0.822371, -0.469302, 0.343922, 1.41374, -0.65231, -1.45734, -1.48847, -1.00997, 0.394152, -0.245409, 1.62749, 0.0605587, 1.01268, 1.51944, 0.431856, 1.7025, -0.434982, -0.957084, -0.0629937, -1.99956, 0.014591, 0.389466, 0.849428, -0.0900073, 0.590357, -1.18661}; Alpha = Table[b1, {Mw}]; result4 = Table[Norm[trainwstd[[i]] - trainwstd[[j]]], {i, 1, Mw}, {j, 1, Mw}]; result5 = Exp[-(result4^2)*Alpha*0.5]; f = Table[Sum[result5[[i, j]], {j, 1, Mw}], {i, 1, Mw}]; ff = Table[D[f[[i]], b1], {i, 1, Mw}]; Alpha1 = Table[b1 + (f[[1]] - f[[j]])/ff[[j]], {j, 2, Mw}]; result0 = Table[Norm[trainwstd[[j]] - trainwstd[[i]]], {j, 2, Mw}, {i, 1, j - 1}]; result11 = Table[trainLastd[[i]]* Exp[-(result0[[j - 1, i]]^2)*Alpha1[[j - 1]]*0.5], {j, 2, Mw}, {i,1, j - 1}]; result1 = PadRight[result11]; result22 = Table[Exp[-(result0[[j - 1, i]]^2)*Alpha1[[j - 1]]*0.5], {j, 2, Mw}, {i, 1, j - 1}]; result2 = PadRight[result22]; predtrainLastdup = Table[Sum[result1[[i, j]], {j, 1, Mw - 1}], {i, 1, Mw - 1}]; predtrainLastddown = Table[Sum[result2[[i, j]], {j, 1, Mw - 1}], {i, 1, Mw - 1}]; predtrainLastd = predtrainLastdup/predtrainLastddown; result33 = Table[Exp[-2*(result0[[j - 1, i]]^2)], {j, 2, Mw}, {i, 1, j - 1}]; result3 = PadRight[result33]; rou = Table[Sum[result3[[i, j]], {j, 1, Mw - 1}], {i, 1, Mw - 1}]; matrixb2 =Table[rou[[i]]*(trainLastd[[i + 1]] - predtrainLastd[[i]])^2, {i, 1, Mw - 1}]; b2 = Sum[matrixb2[[i]]/(Mw - 1), {i, 1, Mw - 1}]; postp = -Log[b2^(-(Mw - 1)/2)]*Exp[-(Mw - 1)/2]; Plot[postp, {b1, 0, 50}]optb1 = FindMinimum[{postp, 0.0001 < b1 < 50}, b1] {0.0000132482, {b1 -> 5.33917}}

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