A contactless position and/or distance sensor for determining the distance, the spatial orientation, the material properties, or the like of a target object, and a method for operating the same, uses at least two sensor elements, which form a sensor module, Signals provided by the at least two sensor elements are jointly evaluated using at least one artificial neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-19. (canceled) 20. Contactless position and/or distance sensor for determining the distance or the spatial orientation of a target object, wherein at least two sensor elements forming a sensor module (100) are provided and sensor signals (103) delivered from the at least two sensor elements are jointly evaluated by means of an artificial neural network (110), wherein the sensor module (100) is connected to a pre-processing module (105), such that signals and/or data can be exchanged therebetween, for pre-processing the at least two sensor signals (103) delivered from the sensor module (100), and wherein the pre-processed signals (107) are supplied to the artificial neural network (110). 21. Position and/or distance sensor according to claim 20, wherein the at least two sensor elements can be influenced by respective measured variables and possible measurement and/or ambient conditions. 22. Position and/or distance sensor according to claim 21, wherein the at least two sensor elements are each based on physically different operating principles. 23. Position and/or distance sensor according to claim 21, wherein the at least two sensor elements are based on physically equivalent or similar operating principles and each have different characteristic curves. 24. Position and/or distance sensor according to claim 20, wherein the at least two sensor elements are operated statically or dynamically. 25. Position and/or distance sensor according to claim 24, wherein the aforementioned dynamic operation corresponds to a pulsed operation. 26. Position and/or distance sensor according to claim 20, wherein the at least one artificial neural network (110) is trained by a calibration or learning process in respect of the sensor signals (103) delivered from the at least two sensor elements. 27. Position and/or distance sensor according to claim 26, wherein, in the case of the calibration or learning process, the at least one artificial neural network (110) is trained on the corresponding measured variable in different measurement and/or ambient conditions. 28. Position and/or distance sensor according to claim 20, wherein the output signals of the artificial neural network (110) are supplied to a post-processing module (115), by means of which output signals (113) delivered from the artificial neural network (110) are post-processed by means of an output module (120) for the corresponding display. 29. Position and/or distance sensor according to claim 20, wherein two artificial neural networks (400, 405) are provided, wherein sensor signals (410) delivered from the sensor elements are supplied to the first of the two artificial neural networks (400), the output data (415) of the first artificial neural network (400) are supplied to a pre-processing module (420), and the data pre-processed in this way are supplied (430) to the second of the two artificial neural networks (405). 30. Position and/or distance sensor according to claim 29, wherein the sensor signals (410) delivered from the sensor elements are additionally supplied (425) to the pre-processing module (420). 31. Method for operating a contactless position and/or distance sensor according to claim 20, wherein the at least one artificial neural network (110) is trained by a calibration or learning process in respect of the sensor signals (103) supplied from the at least two sensor elements. 32. Method according to claim 31, wherein, in the case of the calibration or learning process, the at least one artificial neural network (110) is trained for each of the at least two sensor elements on the corresponding measured variable in different conditions. 33. Method according to claim 31, wherein the at least one artificial neural network (110) is trained such that it extracts, from the pre-processed sensor signals (107), specific parameters which correspond to corresponding parameters of the target object to be detected. 34. Method according to claim 33, wherein the aforementioned parameters concern the distance between the sensor module (100) and the target object, the position of the target object above the sensor elements, or the surface properties of the target object. 35. Method according to claim 31, wherein the at least one artificial neural network (110) converts the pre-processed sensor signals (107) into output signals (113) which correlate with the aforementioned properties of the target object. 36. Method according to claim 31, wherein the at least one artificial neural network (110) delivers output signals which indicate the signal quality of the sensor signals (103) delivered from the at least two sensor elements. 37. Method according to claim 31, wherein the sensor signals (103) delivered from the at least two sensor elements constitute static or dynamic waveforms and serve as input signals for the at least one artificial neural network.