Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx
Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx
... Automotive Applications 11 7
0 50 10 0 15 0 200 250 300 350 400
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0 50 10 0 15 0 200 250 300 350 400
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Testing
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Fig. 11 . The RNN results after training. The segment from ... 19 97, pp. 77 3 77 8.
75 . D. Prokhorov, “Toward effective combination of off-line and on-line training in ADP framework,” in Proceedings
of the 20 07 IEEE Symp...
Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx
... Networks in Automotive Applications
Danil Prokhorov 10 1
1 Models 10 1
2 VirtualSensors 10 3
3 Controllers 10 6
4 TrainingNN 11 1
5 RNN: AMotivatingExample 11 6
6 VerificationandValidation (V &V) 11 8
References ... 11 8
References 11 9
On Learning Machines for Engine Control
G´erard Bloch, Fabien Lauer, and Guillaume Colin 12 5
1 Introduction 12 5
1. 1 CommonFeatures...
Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf
... F09-F10) 32 70
Pupil-diameter features only (F09-F10) 2 61
Driving-performance features (F 01- F08) 8 60
PleaserefertoTable2forthefeatureindices
0
2
4
6
8
10
12
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
Feature ... 2.0)
F 01 <= -1. 563
F15 <= 0. 0 17 4
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F 21 > 0.0635...
Computational Intelligence in Automotive Applications Episode 1 Part 3 ppt
... (s)
1 394 (two intervals: 18 0 + 214 ) 516 910
2 90 (one interval) 210 300
3 0 240 240
4 15 5 (one interval) 17 5 330
5 16 0 (one interval) 393 553
6 18 0 (one interval) 370 550
7 310 (two intervals: 15 0 ... 550
7 310 (two intervals: 15 0 + 16 0) 6 31 9 41
8 842 (two intervals: 390 + 452) 76 5 1, 6 07
9 210 (two intervals: 75 + 13 5) 255 465
10 673 (two intervals: 310...
Computational Intelligence in Automotive Applications Episode 1 Part 4 doc
... controls
74 % Eating a meal/snack
51% Using a cell phone
41% Tending to children
34% Reading a map/publication
19 % Grooming
11 % Prepared for work
Activities drivers engage in while driving
each. ... on Artificial Intel ligence, Montreal, Canada, 19 95.
11 . I. Guyon and A. Elisseeff. An introduction to feature selection. Journal of Machine Learning Research, 3 :11 57
11 82, 2003.
1...
Computational Intelligence in Automotive Applications Episode 1 Part 5 docx
... Machine Intelligence, pp. 918 –923, July 2003.
31. D.V. Prokhorov. Neural Networks in Automotive Applications. Computational Intelligence in Automotive
Applications, Studies in Computational Intelligence, ... Steinbrecher et al.: Application of Graphical Models in the Automotive Industry, Studies in Computational Intelligence (SCI) 13 2,
79 –88 (2008)
www.spri...
Computational Intelligence in Automotive Applications Episode 1 Part 6 doc
... Tool in (4)
18 2 0. 81 Tool not in (4) and shaft in (2)
18 3 0. 81 Tool not in (1) and location in (right)
210 0.84 Tool in (1, 2)
236 0.85 Tool in (2,4)
240 0. 81 Tool in (1, 4)
244 0. 81 Location in ...
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inputs
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Computational Intelligence in Automotive Applications Episode 1 Part 8 potx
... 0.8 1
0
10
20
30
0.4 0.6 0.8 1
0
10
20
30
0.4 0.6 0.8 1
0
10
20
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Computational Intelligence in Automotive Applications Episode 2 Part 4 pptx
... 2 67. 7 10 0.030 .7
Slope 3 73 .990 .14 5.8
Slope 4 10 0.0 37. 499.8
Bin 1 83.6 31. 376 .2
Bin 2 90. 71 6 .088 .7
Bin 3 89. 614 .5 10 0.0
Bin 4 92 .1 100. 014 .4
Bin 5 98 .12 0.698.6
In order to reduce the dimensionality ... 99. 878 .683.0
Minimum 94. 613 .0 10 0.0
Mean 98. 313 .7 10 0.0
Standard deviation 74 .960. 372 .2
Range 10 0.038. 275 .0
Root mean square (RMS) 92 .11 4....
Computational Intelligence in Automotive Applications Episode 2 Part 7 potx
... (RBF)
kernel, 13 0, 13 9
networks, 10 3, 10 4, 12 6, 12 9, 13 0
Random Forest, 45–48, 54–56
Real-rime recurrent learning (RTRL), 11 1
Recurrent Neural Network (RNN), 10 1, 10 2, 10 5, 10 9,
11 1, 11 6, 14 6, 14 9 15 1, 15 3, ... 206, 2 07
Multilayer perceptron (MLP), 10 2, 10 3, 12 6, 12 8
Near-IR, 21, 22, 24, 33
Neural network, 69, 17 8, 17 9, 18 5
controller, 10 6,...
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