Computational Intelligence in Automotive Applications Episode 1 Part 8 potx

Computational Intelligence in Automotive Applications Episode 1 Part 8 potx

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 30 0.4 0.6 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 30 N e = 10 00 rp m OF =0 ◦ CA/m OF =0. 41 ◦ CA/m OF =1. 16 ◦ CA/m N e = ... and Control in Spark Ignition Automotive Engines, Studies in Computational Intelligence (SCI) 13 2, 14 5 16 8 (20 08) www.spri...

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Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx

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 CommonFeat...

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Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf

Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf

... 85 . 780 . 686 .4 81 . 87 7 .88 2. 488 . 087 .9 81 . 393.490 .88 8.4 85 .4 Standard deviation (%) 0.70 .80 .60 .80 .90.90.70.70 .80 .40.50.50.69 Table 8. The average correct estimation rates of driver-dependent training ... [0.794] Rule 1/ 7: (9.9 /1. 5, lift 2.0) F 01 <= -1. 563 F15 <= 0. 017 4 -> class High [0. 788 ] Rule 1/ 8: (11 .5/3 .1, lift 1. 8) F15 > 0. 01...

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Computational Intelligence in Automotive Applications Episode 1 Part 3 ppt

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) 765 1, 607 9 210 (two intervals: 75 + 13 5) 255 465 10 673 (two intervals: 310 + 363)...

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Computational Intelligence in Automotive Applications Episode 1 Part 4 doc

Computational Intelligence in Automotive Applications Episode 1 Part 4 doc

... transcriptions in real time. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 88) , pp. 611 – 614 , New York City, USA, April 11 14 , 19 88 . 28. K. Torkkola, ... steering wheel and accelerator pedal Variable Importance steeringWheel 10 0.00 accelerator 87 .34 steeringWheel rv9 68. 89 steeringWheel ent15 ra9 58. 44 stat3 of steer...

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Computational Intelligence in Automotive Applications Episode 1 Part 5 docx

Computational Intelligence in Automotive Applications Episode 1 Part 5 docx

... Machine Intelligence, pp. 9 18 –923, July 2003. 31. D.V. Prokhorov. Neural Networks in Automotive Applications. Computational Intelligence in Automotive Applications, Studies in Computational Intelligence, ... Pattern Analysis and Machine Intelligence, 28 (11 ) : 18 63 18 68, 2006. 28. C. Papageorgiou and T. Poggio. A trainable system for object detection. Internation...

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Computational Intelligence in Automotive Applications Episode 1 Part 6 doc

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 ... X 0 12 3 0 .85 Tool in (2,4) and location in (left) 12 6 0 .86 Shaft in (2) and location in (right) 12 7 0 .83 Tool in (2,3) and shaft in (2) 13...

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Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx

Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx

... et al.: On Learning Machines for Engine Control, Studies in Computational Intelligence (SCI) 13 2, 12 5 14 4 (20 08) www.springerlink.com c  Springer-Verlag Berlin Heidelberg 20 08 11 6 D. Prokhorov transformation ... Automotive Applications 11 7 0 50 10 0 15 0 200 250 300 350 400 1 −0.5 0 0.5 1 0 50 10 0 15 0 200 250 300 350 400 1 −0.5 0 0.5 1 Testing Training Fi...

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Computational Intelligence in Automotive Applications Episode 2 Part 7 potx

Computational Intelligence in Automotive Applications Episode 2 Part 7 potx

... 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, 10 8, 11 0, 11 2 in engine control, 12 6 models, 10 3, 11 6, 12 8 Neuro-fuzzy inference system, ... (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...

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Computational Intelligence in Automotive Applications Episode 2 Part 1 docx

Computational Intelligence in Automotive Applications Episode 2 Part 1 docx

... 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Time [s] AFR [/] (Case 3) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 21 Time [s] AFR [/] (Case 4) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Time ... (Case 1) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Time [s] measured predicted 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Tim...

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