Trajectory Estimation and Control of Vehicle using Neuro-Fuzzy Technique

Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in controlling the non linear system.

• B. Selma
• S. Chouraqui

• Adaptive neuro-fuzzy inference system (ANFIS)
• Fuzzy logic
• neural network
• nonlinear system
• control

[1] Baxt WG. Use of an artificial neural network for data analysis in clinical
decision making: the diagnosis of acute coronary occlusion. Neural
Comput 1990;2:480-9.
[2] Miller AS, Blott BH, Hames TK. Review of neural network applications
in medical imaging and signal processing. Med Biol Eng Comput
1992;30:449-64.
[3] G├╝ler Iİ, ├£beyli ED. Detection of ophthalmic artery stenosis by
leastmean squares backpropagation neural network. Comput Biol Med
2003;33(4):333-43.
[4] ├£beyli ED, G├╝ler Iİ. Neural network analysis of internal carotid arterial
Doppler signals: Predictions of stenosis and occlusion. Expert Syst Appl
2003;25(1):1-13.
[5] Dubois D, Prade H. An introduction to fuzzy systems. Clin Chim Acta
1998;270:3-29.
[6] Kuncheva LI, Steimann F. Fuzzy diagnosis. Artif Intell Med
1999;16:121-8.
[7] Nauck D, Kruse R. Obtaining interpretable fuzzy classification rules
from medical data. Artif Intell Med 1999;16:149-69.
[8] Belal SY, Taktak AFG, Nevill AJ, Spencer SA, Roden D, Bevan S.
Automaticdetection of distorted plethysmogram pulses in neonates and
paediatric patients using an adaptive-network-based fuzzy inference
system. Artif Intell Med 2002;24:149-65.
[9] G├╝ler Iİ, ├£beyli ED. Application of adaptive neuro-fuzzy inference
system for detection of electrocardiographic changes in patients with
partial epilepsy using feature extraction. Expert Syst Appl
2004;27(3):323-30.
[10] ├£beyli ED, G├╝ler Iİ. Automatic detection of erythemato-squamous
diseases using adaptive neuro-fuzzy inference systems. Comput Biol
Med 2005;35(5):421-33.
[11] ├£beyli ED, G├╝ler Iİ. Adaptive neuro-fuzzy inference systems for
analysis of internal carotid arterial Doppler signals. Comput Biol Med
(2005), in press.
[12] Jang J-SR. Self-learning fuzzy controllers based on temporal
backpropagation. IEEE Trans Neural Netw 1992;3(5):714-23.
[13] Usher J, Campbell D, Vohra J, Cameron J. A fuzzy logic-controlled
classifier for use in implantable cardioverter defibrillators. Pace
PacingClin Electrophysiol 1999;22:183-6.
[14] Virant-Klun I, Virant J. Fuzzy logic alternative for analysis in the
biomedical sciences. Comput Biomed Res 1999;32:305-21.
[15] Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE.
Indications of nonlinear deterministic and finite-dimensional structures
in time seires of brain electrical activity: dependence on recording
region and brain state. Phys Rev E 2001;64:061907.
[16] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning
internal representations by error propagation. In D. E. Rumelhart & J. L.
McClelland(Eds.), Parallel distributed processing. Cambridge, MA:
MIT Press.
[17] Jang J-SR. ANFIS: Adaptive-network-based fuzzy inference
system.IEEE Trans Syst Man Cybern 1993;23(3):665-85.