Motor vehicle control device provided with a neuronal network

• US 20070203616A1
• Filed: 06/24/2005
• Published: 08/30/2007
• Est. Priority Date: 06/25/2004
• Status: Abandoned Application

• Current Assignee: FEV Motorentechnik GmbH
• Original Assignee: FEV Motorentechnik GmbH
• Inventors: Borrmann, Eric, Schermus, Christof
• Application Number:
  US11/571,170
• Publication Number:
  US 20070203616A1
• Time in Patent Office: Days
• US Class Current: 701/1
• CPC Class Codes: F02D 41/1405 Neural network control
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/048 Activation functions
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent

Abstract

A vehicle control device with a neural network is provided. The vehicle control device contains at least one backpropagation network and at least one radial basis function. The at least one backpropagation network is coupled to the at least one radial basis function for the purpose of determining a vehicle-specific characteristic diagram, The radial basis function can provide training data to the backpropagation network. The training data can include input data to and output data from the radial basis function.

Claims

1-24. -24. (canceled)

25. A vehicle control device with a neural network comprising:

at least one backpropagation network; and

at least one radial basis function;

said at least one backpropagation network coupled to said at least one radial basis function.

26. The invention of claim 25, wherein said neural network comprises said at least one radial basis function, for the purpose of training said at least one backpropagation network.

27. The invention of claim 25, wherein said at least one radial basis function is directly coupled to said at least one backpropagation network.

28. The invention of claim 25, wherein said neural network is operable in a real-time system.

29. The invention of claim 25, wherein said at least one backpropagation network receives training data from said at least one radial basis function.

30. The invention of claim 26, wherein said at least one backpropagation network receives input data to and training data from said at least one radial basis function.

31. The invention of claim 30, wherein training data for training said at least one backpropagation network includes input data to and output data from said at least one radial basis function.

32. The invention of claim 31, wherein said at least one radial basis function provides virtual data to said at least one backpropagation network.

33. The invention of claim 32, wherein said at least one backpropagation network is directly coupled to said at least one radial basis function.

34. The invention of claim 25, wherein said vehicle control device is an application control device.

35. The invention of claim 34, wherein said application control device is an engine control device.

36. The invention of claim 35, wherein said engine control device affects at least one valve train.

37. The invention of claim 35, wherein said engine control device affects a fuel injection system.

38. The invention of claim 35, wherein said engine control device affects an exhaust behavior.

39. The invention of claim 25, wherein said vehicle control device controls a safety device.

40. The invention of claim 25, wherein said vehicle control device controls a vehicle position.

41. The invention of claim 25, further comprising more than one interconnected vehicle control devices, for the purpose of data exchange and computation of characteristic diagram data for the vehicle.

42. The invention of claim 41, wherein said more than one vehicle control devices are connected in parallel.

43. The invention of claim 25, wherein said vehicle control device is a component of a stationary test stand.

44. The invention of claim 25, wherein said vehicle control device is a component of a stationary engine test stand.

45. The invention of claim 41, further comprising a data medium, said data medium storing said characteristic diagram data, for the purpose of copying said characteristic diagram data onto a different vehicle control device.

46. A method for the production of at least one vehicle-specific characteristic diagram, comprising;

providing at least one radial basis function operable to provide input data;

providing at least one backpropagation network operable to accept the input data;

coupling the at least one radial basis function to the least one backpropagation network for the purpose of processing the input data into at least one vehicle-specific characteristic diagram.

47. The method of claim 46, wherein the at least one radial basis function provides the input data to the at least one backpropagation network, for the purpose of training the at least one backpropagation network.

48. The method of claim 46, further comprising providing a neural network, the neural network simulating the conditions of a vehicle by computation.

49. The method of claim 48, wherein the neural network computes simulated vehicle conditions.

50. The method of claim 48, wherein the neural network simulates data for a vehicle.

51. The method of claim 48, wherein the neural network provides training data for a vehicle control device.

52. The method of claim 46, further comprising providing a control device, the control device producing the at least one vehicle-specific characteristic diagram.

53. The invention of claim 46, wherein the production of at least one vehicle-specific characteristic diagram is used for the diagnosis of at least a part of a vehicle.