1. CPP-NLMS-Testreihe
Paramter:
- Input: 500.000 Pixel (-n 500000)
- WindowSize: 5 (Standard Value)
- Lernrate von 0.1 bis 1.0 (increased by 0.1)
- -i cathedral.ppm
- random seed generator : 3 (-s 3)
1. Local Mean
| Lernrate |
meanerror |
devitation |
| 0.1 |
0.017622 |
264.808714 |
| 0.2 |
0.035715 |
563.756509 |
| 0.3 |
0.054145 |
1058.390726 |
| 0.4 |
0.072960 |
1749.137624 |
| 0.5 |
0.092207 |
2636.592222 |
| 0.6 |
0.111931 |
3721.514220 |
| 0.7 |
0.132180 |
5004.829359 |
| 0.8 |
0.153002 |
6487.641093 |
| 0.9 |
0.174440 |
8171.260237 |
| 1.0 |
0.196529 |
10057.264574 |
2. direct Predecessor
| Lernrate |
meanerror |
devitation |
| 0.1 |
0.034605 |
1165.639836 |
| 0.2 |
0.061290 |
5143.795751 |
| 0.3 |
0.081941 |
14101.954880 |
| 0.4 |
0.098059 |
33151.61923 |
| 0.5 |
0.111109 |
73575.962146 |
| 0.6 |
0.123070 |
159449.145873 |
| 0.7 |
0.138492 |
347221.927325 |
| 0.8 |
519.742508 |
8159987385389.823242 |
| 0.9 |
-7547134082896249132941312.000000 |
3877153348329978955993364733716443880442465820831329026048.000000 |
| 1.0 |
3761239144093188417284117395383469067145028584884793393044299775046771047758036992.000000 |
2074572983654522674987602010560539857768301297996124872466118751856211301657828168212459204392861396853691477732320683100737172969672730282637731961860360049654132492992512.000000 |
3. differential Predecessor
| Lernrate |
meanerror |
devitation |
| 0.1 |
0.002158 |
35.172878 |
| 0.2 |
0.003306 |
33.315664 |
| 0.3 |
0.003656 |
32.549577 |
| 0.4 |
0.003311 |
33.097449 |
| 0.5 |
0.002340 |
35.717416 |
| 0.6 |
0.000869 |
43.040214 |
| 0.7 |
-0.000213 |
71.249809 |
| 0.8 |
0.013732 |
1363.663734 |
| 0.9 |
-0.071016 |
4623896.100036 |
| 1.0 |
-211924.365595 |
556770831730198528.000000 |
2. CS-NLMS-Testreihe
Parameter:
-N:4000 Pixel
-M: 5
-Learnrate: 0.1 bis 1.0 (increased by 0.1)
-Image: boats.y.png
--> Gewichte sind random, daher nicht reproduzierbar!
1. local mean
| Lernrate |
Average error |
Variance of the error |
| 0.1 |
-0,0223811013862385 |
5337,82462718957 |
| 0.2 |
-0,0622259728578006 |
7363,56898611733 |
| 0.3 |
-0,0298976210637875 |
9614,92806843726 |
| 0.4 |
0,0849844894447272 |
12088,2433018213 |
| 0.5 |
0,299055989866592 |
14819,6805234785 |
| 0.6 |
0,585283074155977 |
17874,0914703503 |
| 0.7 |
0,979106442634296 |
21337,9978781998 |
| 0.8 |
1,46391408202542 |
25322,8680485397 |
| 0.9 |
2,05872807071837 |
29964,9719994441 |
| 1,0 |
2,79656379441662 |
35436,9340804565 |
2. direct predecessor
| Lernrate |
Average error |
Variance of the error |
| 0.1 |
0,151005103097187 |
5471,5876773699 |
| 0.2 |
0,606943328690361 |
7729,18121268243 |
| 0.3 |
1,14390553779214 |
10465,2060324616 |
| 0.4 |
1,76810718228813 |
13695,8876087231 |
| 0.5 |
2,5193083296817 |
17431,0433982945 |
| 0.6 |
3,38144695588288 |
21696,5188824818 |
| 0.7 |
4,36367842697108 |
26566,2513099756 |
| 0.8 |
5,51257464442153 |
32193,8755290981 |
| 0.9 |
6,83374986915135 |
38850,115776936 |
| 1.0 |
8,38129514988831 |
46958,0983013679 |
3. differential predecessor
| Lernrate |
Average error |
Variance of the error |
| 0.1 |
-0,0160622191672049 |
4750,33040669542 |
| 0.2 |
0,00640864710641419 |
6041,46065665648 |
| 0.3 |
0,0432263456982662 |
7412,09833871273 |
| 0.4 |
0,106075295986603 |
8891,97578539296 |
| 0.5 |
0,174689052473554 |
10532,273473301 |
| 0.6 |
0,241331954473674 |
12394,8177286223 |
| 0.7 |
0,359973579958283 |
14552,6708886431 |
| 0.8 |
0,494261683495002 |
17102,3682973733 |
| 0.9 |
0,671346960651145 |
20166,0023256068 |
| 1.0 |
0,892045554010017 |
23907,2852270274 |