Trajectory Estimation and Control of Vehicle using Neuro-Fuzzy Technique
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Trajectory Estimation and Control of Vehicle using Neuro-Fuzzy Technique

Authors: B. Selma, S. Chouraqui

Abstract:

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.

Keywords: Adaptive neuro-fuzzy inference system (ANFIS), Fuzzy logic, neural network, nonlinear system, control

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073148

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[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.