A novel algorithm for frequency extraction of ABS signals by using DTDNNs
| Autor: | Mohammad Ali Shafieian, Hamed Banizaman, Shahrzad Sedaghat |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information management
Anti-lock braking system General Computer Science Artificial neural network Computer science Convergence (routing) Control (management) Real-time computing Pulse frequency ABS signal processing pulse frequency neural network intelligent transportations system ABS sensor Extraction (military) Exact location Electrical and Electronic Engineering |
| Zdroj: | Volume: 27, Issue: 3 1752-1764 Turkish Journal of Electrical Engineering and Computer Science |
| ISSN: | 1303-6203 1300-0632 |
| DOI: | 10.3906/elk-1712-397 |
| Popis: | Intelligent transportations system (ITSs) have emerged to increase safety and convenience of people in vehicles. In an ITS, communication devices in the vehicle or along the streets send the information gathered from the vehicle to information management centers as well as sending processed information to the vehicle. Furthermore, it is necessary to locate the exact location of the vehicle on a digital map in order to navigate the vehicle precisely in control and navigation systems. One of the technologies for this purpose is the antilock brake system (ABS), which can avoid accidents effectively and can also be utilized to determine vehicle speed and location by using its pulses. To do so, the frequency of ABS pulses should be extracted. In this paper, a novel method for frequency extraction is introduced in which one type of neural network, the distributed time-delay neural network (DTDNN), is used. Simulation results show that the output of the neural network can acceptably follow frequency variations of ABS signals after convergence. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|