- Tytuł:
- Estimation of the air-fuel mixture ratio, based on the signal from the optical fibre interference sensor, using artificial neuron network
- Autorzy:
- Kamiński, T.
- Tematy:
-
transport
fibre optics sensor
neuron networks - Pokaż więcej
- Wydawca:
- Instytut Techniczny Wojsk Lotniczych
- Powiązania:
- https://bibliotekanauki.pl/articles/244227.pdf Link otwiera się w nowym oknie
- Opis:
- There are a lot of definitions for the "intelligence", however according to the Prof. Jan Strelau the intelligence can only be attributed to a human. Thus, programs or devices thought out by a man can only imitate intelligence. The phras - artificial neuron networks describes programs or electronic devices running mathematical models of pseudo parallel data processing, consisting of many interconnected neurons imitating actions of biological structures of a brain. The neuron networks are used, amongst other things, for the sound and picture recognition, for the predicting, objects classification, data analysis, matching and optimisation. This work describes construction of an artificial neuron network in use. The design and operation principles of a fibre optics interference side - hole sensor are presented for the pressure measurement inside the engine combustion chamber and the data range used during a „teaching" process of an artificial neuron network. The gas pressure in the engine combustion chamber carries a lot of information which can be used to characterize the working cycle process. Knowing the pressure curve it is possible to estimate the air-fuel mixture ratio, detect lack of mixture ignition in the cylinder, explosive knocking combustion, unevenness of the following engine working cycles and even estimate the combustion chamber walls' temperature. Construction of a sensor ensures high pressure sensitivity with a low temperature sensitivity. The measuring head has been placed in the threaded opening made in the engine cylinder head. The paper presents results for two examples of the air-fuel mixture ratio estimations using already developed network. In the first example the measurement data, used during the network educating process, has deliberately been changed at random by 3%. In the second case, the original measurement data has been used. This allowed the initial assessment of the measurement noise influence on the mixture content estimation using fibre optics pressure sensor, in the combustion chamber, combined with the artificial neuron networks. The mixture content estimation results, together with the data obtained during measurements using wide range oxygen sensor, which are presented on the diagram, allowed the conclusions to be formulated.
- Dostawca treści:
- Biblioteka Nauki
Artykuł