Increase of accuracy of linear accelometers using of artificial neural network

• Autorzy: Bezvesilnaja E. , Podchashinsky Y.
• Języki publikacji: EN

Abstrakty

EN

In the article the algorithmic method of increase of accuracy of linear accelerometers is considered. In the gyroscopic accelerometer the angle of deviation of a sensing element consists of a constant and variable making. The constant making is proportional to acceleration, which is considered constant on an interval of one measurement. Variable making is determined by precessions of a sensing element. In conditions of presence of correlated distortion of determined and random character it is necessary with high accuracy to define values of a constant making. This problem is solved in the article because of method of a maximum probability. The realization of algorithm of identification of an angular rule of a sensing element because of artificial neural network application is offered. This network contains a delay line and three adaptive linear neurons. The procedures of training and adaptation of a network provide additional error reduction in non-stationary and unfavorable conditions. The obtained outcomes can be used for a construction of high-precision navigational and gravimetric systems.

Słowa kluczowe

EN

linear accelerometer; accuracy; artificial neural networks

PL

akcelerometr liniowy; przyspieszeniomierz; dokładność; sztuczne sieci neuronowe

• Wydawca: Oficyna Wydawnicza Politechniki Rzeszowskiej
• Czasopismo: Zeszyty Naukowe Politechniki Rzeszowskiej. Mechanika
• Rocznik: 2010
• Tom: z. 80 [274]
• Strony: 17--24
• Opis fizyczny: Bibliogr. 4 poz., rys.

Twórcy

autor

Bezvesilnaja E.

autor

Podchashinsky Y.

• National Technical University of Ukraine "KPI", Kiev

Bibliografia

• 1. Безвесільна О.М.: Авіаційні гравіметричні системи та гравіметри: підручник. Житомир, ЖДТУ, 2007.
• 2. Синицын И.Н.: Фильтры Кальмана и Пугачева: учебное пособие. М.: Университецкая книга; Логос, 2006.
• 3. Кузьмин С.З.: Основы тeории цифровой обработки радиолокационной информации. Советское радио, 1974.
• 4. Руденко О.Г.: Штучні нейронні мережі: навчальний посібник. Харків: ТОВ “Компанія” СМІТ, 2009.