| Title |
Neuro-fuzzy controller to navigate an unmanned vehicle
|
|---|---|
| Published in |
SpringerPlus, April 2013
|
| DOI | 10.1186/2193-1801-2-188 |
| Pubmed ID | |
| Authors |
Boumediene Selma, Samira Chouraqui |
| Abstract |
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 24% |
| Student > Ph. D. Student | 7 | 21% |
| Student > Doctoral Student | 4 | 12% |
| Professor | 3 | 9% |
| Researcher | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 5 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 14 | 42% |
| Computer Science | 6 | 18% |
| Mathematics | 2 | 6% |
| Business, Management and Accounting | 1 | 3% |
| Economics, Econometrics and Finance | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 6 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2013.
All research outputs
#14,170,673
of 22,711,645 outputs
Outputs from SpringerPlus
#769
of 1,852 outputs
Outputs of similar age
#109,333
of 193,471 outputs
Outputs of similar age from SpringerPlus
#32
of 109 outputs
Altmetric has tracked 22,711,645 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,852 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 55% of its peers.
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We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.