## A.2 Par apprentissage indirect hors-ligne (réseau 3-9-2)

couche 2

neurone 1 -- entree +0.000000
w12(1,1) = -0.723065
w12(2,1) = +1.154869
w12(3,1) = -1.228074
w12(seuil,1) = +0.050198

neurone 2 -- entree +0.000000
w12(1,2) = +1.191010
w12(2,2) = -1.610935
w12(3,2) = -0.597496
w12(seuil,2) = -0.023970

neurone 3 -- entree +0.000000
w12(1,3) = +0.410392
w12(2,3) = -0.107543
w12(3,3) = -1.685538
w12(seuil,3) = +0.005864

neurone 4 -- entree +0.000000
w12(1,4) = -0.436845
w12(2,4) = -0.527577
w12(3,4) = -1.539789
w12(seuil,4) = -0.056657

neurone 5 -- entree +0.000000
w12(1,5) = +0.297137
w12(2,5) = -0.664976
w12(3,5) = -1.120319
w12(seuil,5) = +0.012673

neurone 6 -- entree +0.000000
w12(1,6) = +0.406139
w12(2,6) = -1.307570
w12(3,6) = -1.582946
w12(seuil,6) = +0.075353

neurone 7 -- entree +0.000000
w12(1,7) = +1.359773
w12(2,7) = +1.500983
w12(3,7) = -0.521676
w12(seuil,7) = +0.035080

neurone 8 -- entree +0.000000
w12(1,8) = -0.564598
w12(2,8) = -0.896487
w12(3,8) = -0.160246
w12(seuil,8)= +0.018847
neurone 9 -- entree +0.000000
w12(1,9)= -1.545528
w12(2,9)= +0.839965
w12(3,9)= -0.743622
w12(seuil,9)= -0.021736

couche 3

neurone 1 -- entree +0.000000
w23(1,1)= +0.871386
w23(2,1)= +0.705845
w23(3,1)= +0.810549
w23(4,1)= +1.477265
w23(5,1)= +0.374727
w23(6,1)= -0.601673
w23(7,1)= +0.692437
w23(8,1)= +1.042243
w23(9,1)= +0.564070
w23(seuil,1)= -0.004699
neurone 2 -- entree +0.000000
w23(1,2)= +0.489709
w23(2,2)= -0.294968
w23(3,2)= -1.301873
w23(4,2)= +0.348595
w23(5,2)= -1.621301
w23(6,2)= -1.105606
w23(7,2)= -1.280132
w23(8,2)= +0.476986
w23(9,2)= -1.300904
w23(seuil,2)= -0.004536

## A.3 Par apprentissage indirect en-ligne sur simulateur

couche 2

neurone 1 -- entree +0.000000
w12(1,1)= -0.921255
w12(2,1)= +0.994591
w12(3,1)= -1.538290
w12(seuil,1)= -0.086154
neurone 2 -- entree +0.0000000
w12(1,2)= +1.170383
w12(2,2)= -1.621621
w12(3,2)= -0.732577
w12(seuil,2)= +0.058734

neurone 3 -- entree +0.000000
w12(1,3)= +0.570560
w12(2,3)= +0.385995
w12(3,3)= -2.052435
w12(seuil,3)= +0.169975

neurone 4 -- entree +0.000000
w12(1,4)= -0.839715
w12(2,4)= -0.470524
w12(3,4)= -1.490873
w12(seuil,4)= +0.030061

neurone 5 -- entree +0.000000
w12(1,5)= +0.495849
w12(2,5)= -0.274613
w12(3,5)= -1.441139
w12(seuil,5)= +0.157394

neurone 6 -- entree +0.000000
w12(1,6)= +0.818888
w12(2,6)= -1.056968
w12(3,6)= -1.435846
w12(seuil,6)= -0.306248

neurone 7 -- entree +0.000000
w12(1,7)= +1.286090
w12(2,7)= +1.735243
w12(3,7)= -1.049188
w12(seuil,7)= +0.265697

neurone 8 -- entree +0.000000
w12(1,8)= -0.936837
w12(2,8)= -0.684941
w12(3,8)= -0.259147
w12(seuil,8)= +0.063222

neurone 9 -- entree +0.000000
w12(1,9)= -1.347320
w12(2,9)= +0.982884
w12(3,9)= -1.368425
w12(seuil,9)= -0.668708

couche 3
neurone 1 -- entree +0.000000
w23(1,1)= +1.423134
w23(2,1)= +0.638445
w23(3,1)= +0.980350
w23(4,1)= +1.671976
w23(5,1)= +0.493188
w23(6,1)= -0.642322
w23(7,1)= +0.798575
w23(8,1)= +1.258252
w23(9,1)= +1.346807
w23(seuil,1)= +0.087882

neurone 2 -- entree +0.000000
w23(1,2)= +0.184954
w23(2,2)= -0.242906
w23(3,2)= -1.685603
w23(4,2)= +0.359647
w23(5,2)= -1.737275
w23(6,2)= -0.958843
w23(7,2)= -1.875649
w23(8,2)= +0.683215
w23(9,2)= -1.521405
w23(seuil,2)= -0.137071

## A.4 Par apprentissage indirect en-ligne sur le robot

### A.4.1 Après le premier apprentissage

couche 2

neurone 1 -- entree +0.000000
w12(1,1)= -1.247891
w12(2,1)= +1.095074
w12(3,1)= +0.459951
w12(seuil,1)= +0.118040

neurone 2 -- entree +0.000000
w12(1,2)= +0.871893
w12(2,2)= -0.994418
w12(3,2)= +0.768162
w12(seuil,2)= -0.114644

neurone 3 -- entree +0.000000
w12(1,3)= -1.985344
w12(2,3)= +1.141364
w12(3,3)= -1.191916
w12(seuil,3)= -0.081949

neurone 4 -- entree +0.000000
w12(1,4) = -0.799609
w12(2,4) = -0.913088
w12(3,4) = +0.997954
w12(seuil,4) = +0.008675
neurone 5 -- entree +0.000000
w12(1,5) = +1.522162
w12(2,5) = +1.294514
w12(3,5) = +0.874330
w12(seuil,5) = +0.055672
neurone 6 -- entree +0.000000
w12(1,6) = -1.597675
w12(2,6) = +1.808000
w12(3,6) = +0.639672
w12(seuil,6) = -0.042556
neurone 7 -- entree +0.000000
w12(1,7) = +1.630451
w12(2,7) = -0.362007
w12(3,7) = -2.359884
w12(seuil,7) = +0.033331
neurone 8 -- entree +0.000000
w12(1,8) = -1.034568
w12(2,8) = +0.333602
w12(3,8) = -0.933808
w12(seuil,8) = -0.053599
neurone 9 -- entree +0.000000
w12(1,9) = -1.371726
w12(2,9) = +0.788733
w12(3,9) = -0.387971
w12(seuil,9) = +0.076949

couche 3

neurone 1 -- entree +0.000000
w23(1,1) = -1.262644
w23(2,1) = -1.009672
w23(3,1) = +1.782792
w23(4,1) = -1.612177
w23(5,1) = -1.015736
w23(6,1) = -0.138948
w23(7,1) = +1.626661
w23(8,1) = -0.016220
w23(9,1) = +1.942994
w23(seuil,1) = +0.052933
neurone 2 -- entree +0.000000
w23(1,2) = -1.913384
w23(2,2) = -1.158559
w23(3,2) = -0.384870
w23(4,2) = -0.599047
w23(5,2) = -0.956244
w23(6,2) = +0.857367
w23(7,2) = -2.314380
w23(8,2) = -1.267303
w23(9,2) = +2.002006
w23(seuil,2) = +0.013711

### A.4.2 Après le deuxième apprentissage

couche 2

neurone 1 -- entree +0.000000
w12(1,1) = -1.141339
w12(2,1) = +1.145727
w12(3,1) = +0.346131
w12(seuil,1) = +0.085889

neurone 2 -- entree +0.000000
w12(1,2) = +0.939689
w12(2,2) = -0.962537
w12(3,2) = +0.722498
w12(seuil,2) = -0.122138

neurone 3 -- entree +0.000000
w12(1,3) = -1.958023
w12(2,3) = +1.106431
w12(3,3) = -1.441932
w12(seuil,3) = -0.004864

neurone 4 -- entree +0.000000
w12(1,4) = -0.790525
w12(2,4) = -0.887573
w12(3,4) = +1.105576
w12(seuil,4) = -0.007324

neurone 5 -- entree +0.000000
w12(1,5) = +1.530664
w12(2,5) = +1.287392
w12(3,5) = +0.854750
w12(seuil,5) = +0.126433

neurone 6 -- entree +0.000000
w12(1,6) = -1.645146
w12(2,6) = +1.789452
w12(3,6) = +0.784412
w12(seuil,6) = +0.016628

neurone 7 -- entree +0.000000
```
w12(1,7)= +1.751492
w12(2,7)= -0.350591
w12(3,7)= -2.886734
w12(seuil,7)= -0.027841
neurone 8 -- entree +0.000000
w12(1,8)= -0.966868
w12(2,8)= +0.337581
w12(3,8)= -1.119435
w12(seuil,8)= -0.009160
neurone 9 -- entree +0.000000
w12(1,9)= -1.460341
w12(2,9)= +0.754151
w12(3,9)= -0.313292
w12(seuil,9)= +0.039404

couche 3
neurone 1 -- entree +0.000000
w23(1,1)= -1.307625
w23(2,1)= -1.065540
w23(3,1)= +1.884200
w23(4,1)= -1.690037
w23(5,1)= -1.108793
w23(6,1)= -0.246600
w23(7,1)= +1.907510
w23(8,1)= +0.092798
w23(9,1)= +1.947879
w23(seuil,1)= +0.087009
neurone 2 -- entree +0.000000
w23(1,2)= -1.800927
w23(2,2)= -1.101242
w23(3,2)= -0.412815
w23(4,2)= -0.439876
w23(5,2)= -0.915873
w23(6,2)= +1.000183
w23(7,2)= -2.677028
w23(8,2)= -1.332278
w23(9,2)= +2.038303
w23(seuil,2)= -0.002070
```

### A.4.3 Après le troisième apprentissage

couche 2
neurone 1 -- entree +0.000000
w12(1,1)= -1.219313
<table>
  <tbody>
    <tr>
      <td>neurone</td>
      <td>2</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(2,1)</td>
      <td>=</td>
      <td>+1.148808</td>
      <td>w12(3,1)</td>
      <td>=</td>
      <td>+0.104437</td>
    </tr>
    <tr>
      <td>w12(seuil,1)</td>
      <td>=</td>
      <td>+0.147420</td>
      <td>w12(seuil,2)</td>
      <td>=</td>
      <td>-0.140885</td>
    </tr>
    <tr>
      <td>neurone</td>
      <td>3</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(1,2)</td>
      <td>=</td>
      <td>+0.957653</td>
      <td>w12(2,3)</td>
      <td>=</td>
      <td>+1.114656</td>
    </tr>
    <tr>
      <td>w12(3,2)</td>
      <td>=</td>
      <td>+0.665227</td>
      <td>w12(3,3)</td>
      <td>=</td>
      <td>-1.586952</td>
    </tr>
    <tr>
      <td>w12(seuil,3)</td>
      <td>=</td>
      <td>-0.226358</td>
      <td>w12(seuil,4)</td>
      <td>=</td>
      <td>+0.124707</td>
    </tr>
    <tr>
      <td>neurone</td>
      <td>4</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(1,4)</td>
      <td>=</td>
      <td>-0.808583</td>
      <td>w12(2,4)</td>
      <td>=</td>
      <td>-0.892904</td>
    </tr>
    <tr>
      <td>w12(3,4)</td>
      <td>=</td>
      <td>+1.110068</td>
      <td>w12(3,5)</td>
      <td>=</td>
      <td>+0.836488</td>
    </tr>
    <tr>
      <td>w12(seuil,4)</td>
      <td>=</td>
      <td>+0.124707</td>
      <td>w12(seuil,5)</td>
      <td>=</td>
      <td>+0.126031</td>
    </tr>
    <tr>
      <td>neurone</td>
      <td>5</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(1,5)</td>
      <td>=</td>
      <td>+1.553989</td>
      <td>w12(2,5)</td>
      <td>=</td>
      <td>+1.286352</td>
    </tr>
    <tr>
      <td>w12(3,5)</td>
      <td>=</td>
      <td>+0.836488</td>
      <td>w12(3,6)</td>
      <td>=</td>
      <td>+1.005962</td>
    </tr>
    <tr>
      <td>w12(seuil,5)</td>
      <td>=</td>
      <td>+0.126031</td>
      <td>w12(seuil,6)</td>
      <td>=</td>
      <td>+0.023241</td>
    </tr>
    <tr>
      <td>neurone</td>
      <td>6</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(1,6)</td>
      <td>=</td>
      <td>-1.563706</td>
      <td>w12(2,6)</td>
      <td>=</td>
      <td>+1.784328</td>
    </tr>
    <tr>
      <td>w12(3,6)</td>
      <td>=</td>
      <td>+1.005962</td>
      <td>w12(3,7)</td>
      <td>=</td>
      <td>-3.385240</td>
    </tr>
    <tr>
      <td>w12(seuil,6)</td>
      <td>=</td>
      <td>+0.023241</td>
      <td>w12(seuil,7)</td>
      <td>=</td>
      <td>-0.274845</td>
    </tr>
    <tr>
      <td>neurone</td>
      <td>7</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(1,7)</td>
      <td>=</td>
      <td>+1.614114</td>
      <td>w12(2,7)</td>
      <td>=</td>
      <td>-0.330399</td>
    </tr>
    <tr>
      <td>w12(3,7)</td>
      <td>=</td>
      <td>-3.385240</td>
      <td>w12(3,8)</td>
      <td>=</td>
      <td>-1.281440</td>
    </tr>
    <tr>
      <td>w12(seuil,7)</td>
      <td>=</td>
      <td>-0.274845</td>
      <td>w12(seuil,8)</td>
      <td>=</td>
      <td>-0.211968</td>
    </tr>
    <tr>
      <td>neurone</td>
      <td>8</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(1,8)</td>
      <td>=</td>
      <td>-0.910648</td>
      <td>w12(2,8)</td>
      <td>=</td>
      <td>+0.344829</td>
    </tr>
    <tr>
      <td>w12(3,8)</td>
      <td>=</td>
      <td>-1.281440</td>
      <td>w12(3,8)</td>
      <td>=</td>
      <td>-0.181680</td>
    </tr>
    <tr>
      <td>w12(seuil,8)</td>
      <td>=</td>
      <td>-0.211968</td>
      <td>w12(seuil,9)</td>
      <td>=</td>
      <td>+0.048121</td>
    </tr>
    <tr>
      <td>neurone</td>
      <td>9</td>
      <td>--</td>
      <td>entree</td>
      <td>+0.000000</td>
    </tr>
    <tr>
      <td>w12(1,9)</td>
      <td>=</td>
      <td>-1.468110</td>
      <td>w12(2,9)</td>
      <td>=</td>
      <td>+0.753620</td>
    </tr>
    <tr>
      <td>w12(3,9)</td>
      <td>=</td>
      <td>-0.181680</td>
      <td>w12(3,9)</td>
      <td>=</td>
      <td>-0.181680</td>
    </tr>
    <tr>
      <td>w12(seuil,9)</td>
      <td>=</td>
      <td>+0.048121</td>
      <td>w12(seuil,9)</td>
      <td>=</td>
      <td>+0.048121</td>
    </tr>
  </tbody>
</table>
couche 3

neurone 1 -- entree +0.000000
w23(1,1) = -1.303478
w23(2,1) = -1.104927
w23(3,1) = +1.987708
w23(4,1) = -1.732514
w23(5,1) = -1.172579
w23(6,1) = -0.265901
w23(7,1) = +2.051366
w23(8,1) = +0.187796
w23(9,1) = +1.971389
w23(seuil,1) = +0.029691

neurone 2 -- entree +0.000000
w23(1,2) = -1.842621
w23(2,2) = -0.938755
w23(3,2) = -0.642426
w23(4,2) = -0.264940
w23(5,2) = -0.728572
w23(6,2) = +1.040004
w23(7,2) = -3.123444
w23(8,2) = -1.578552
w23(9,2) = +1.923349
w23(seuil,2) = -0.029967