Age	Weight
15	21,66
15	22,75
15	22,3
18	31,25
28	44,79
29	40,55
37	50,25
37	46,88
44	52,03
50	63,47
50	61,13
60	81
61	73,09
64	79,09
65	79,51
65	65,31
72	71,9
75	86,1
75	94,6
82	92,5
85	105
91	101,7
91	102,9
97	110
98	104,3
125	134,9
142	130,68
142	140,58
147	155,3
147	152,2
150	144,5
159	142,15
165	139,81
183	153,22
192	145,72
195	161,1
218	174,18
218	173,03
219	173,54
224	178,86
225	177,68
227	173,73
232	159,98
232	161,29
237	187,07
246	176,13
258	183,4
276	186,26
285	189,66
300	186,09
301	186,7
305	186,8
312	195,1
317	216,41
338	203,23
347	183,38
354	189,7
357	195,31
375	202,63
394	224,82
513	203,3
535	209,7
554	233,9
591	234,7
648	244,3
660	231
705	242,4
723	230,77
756	242,57
768	232,12
860	246,7

The data presented in Table P4.17 show the weights of eye lenses of wild Australian rabbits as a function of age. No simple analytical function can exactly interpolate these data, because we do not have a single-valued function. Instead, we have a nonlinear least-squares model of this data set, using a negative exponential, as described by y = 233.846(1 exp(-0.006042x))+ where is an error term. Using the back-propagation algorithm, design a multilayer perceptron that provides a non linear least-squares approximation to this data set. Compare your result against the least-squares model described. TABLE P4.17 Weights of Eye Lenses of Wild Australian Rabbits Ages Weights Ages Weights Ages Weights (days) (mg) (days) (mg) (days) (mg) 15 21.66 75 94.6 218 174.18 15 22.75 82 92.5 218 173.03 15 22.3 85 105 219 173.54 18 31.25 91 101.7 224 178.86 28 44.79 91 102.9 225 177.68 29 40.55 97 110 227 173.73 37 50.25 98 104.3 232 159.98 37 46.88 125 134.9 232 161.29 44 52.03 142 130.68 237 187.07 63.47 142 140.58 246 176.13 61.13 147 155.3 258 183.4 60 81 147 152.2 276 186.26 61 73.09 144.5 285 189.66 64 79.09 159 142.15 300 186.09 65 79.51 165 139.81 301 186.7 65 65.31 183 153.22 186.8 72 71.9 192 145.72 312 195.1 75 86.1 195 161.1 216.41 Ages (days) 338 347 354 357 375 394 513 535 554 591 648 660 705 723 756 768 860 Weights (mg) 203.23 188.38 189.7 195.31 202.63 224.82 203.3 209.7 233.9 234.7 244.3 231 242.4 230.77 242.57 232,12 246.7 SO SO 150 305 317 The data presented in Table P4.17 show the weights of eye lenses of wild Australian rabbits as a function of age. No simple analytical function can exactly interpolate these data, because we do not have a single-valued function. Instead, we have a nonlinear least-squares model of this data set, using a negative exponential, as described by y = 233.846(1 exp(-0.006042x))+ where is an error term. Using the back-propagation algorithm, design a multilayer perceptron that provides a non linear least-squares approximation to this data set. Compare your result against the least-squares model described. TABLE P4.17 Weights of Eye Lenses of Wild Australian Rabbits Ages Weights Ages Weights Ages Weights (days) (mg) (days) (mg) (days) (mg) 15 21.66 75 94.6 218 174.18 15 22.75 82 92.5 218 173.03 15 22.3 85 105 219 173.54 18 31.25 91 101.7 224 178.86 28 44.79 91 102.9 225 177.68 29 40.55 97 110 227 173.73 37 50.25 98 104.3 232 159.98 37 46.88 125 134.9 232 161.29 44 52.03 142 130.68 237 187.07 63.47 142 140.58 246 176.13 61.13 147 155.3 258 183.4 60 81 147 152.2 276 186.26 61 73.09 144.5 285 189.66 64 79.09 159 142.15 300 186.09 65 79.51 165 139.81 301 186.7 65 65.31 183 153.22 186.8 72 71.9 192 145.72 312 195.1 75 86.1 195 161.1 216.41 Ages (days) 338 347 354 357 375 394 513 535 554 591 648 660 705 723 756 768 860 Weights (mg) 203.23 188.38 189.7 195.31 202.63 224.82 203.3 209.7 233.9 234.7 244.3 231 242.4 230.77 242.57 232,12 246.7 SO SO 150 305 317