|
[1] Christian Lundquist, 2011, Sensor Fusion for Automotive Applications, Linkoping University Electronic Press, Sweden. [2] Robert R. Tenney, Nils R. Sandell, 1980, “Detection with distributed sensors”, Conference on Decision and Control including the Symposium on Adaptive Processes, pp.501-510, Albuquerque, NM, December. [3] Vikas Joshi, et al., 2013, “Information Fusion Based Learning for Frugal Traffic State Sensing”, International joint conference on Artificial Intelligence, pp.2826-2832, China, August. [4] Shashank Shekhar, Oussama Khatib, Makoto Shimojo, 1986, “Sensor Fusion and Object Localizatio”, IEEE International Conference on Robotics and Automation, pp.1623-1628, San Francisco, CA, April. [5] J.K. Suhr, H.G. Jung, Makoto Shimojo, 2018, “Sensor fusion-based precise obstacle localisation for automatic parking systems”, Electronics Letters, Volume: 54, Issue: 7, pp.445-447, March. [6] Chandrajeet Charde, et al., 2015, “Design and Modelling of an Autonomous Automated Guided Vehicle with Intelligent Navigation Control System”, International Journal of Engineering and Technical Research, Volume-3, Issue-3, March. [7] Person I, 1987, “Automated Guided System”, Socio-technical considerations for AGV system implementation, pp.37-42, Brussels, Belgium, October. [8] Jeff E. Fithian, 1993, “A laser-guided, autonomous automated guided vehicle”, Virginia Polytechnic Institute and State University, master degree. [9] Heiko Frohn, Werner v. Seelen, 1989, “An Autonomous Industrial Transport Vehicle Guided by Visual Navigation”, International Conference on Robotics and Automation, pp.1155-1158, Washington, D.C, May. [10] McGillem, C., Rappaport, T. S., 1988, “Infra-Red Location System for Navigation of Autonomous Vehicle”, International Conference on Robotics and Automation, pp.1236-1238, Washington, D.C, April. [11] Stephen Grossberg, 1976, “Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors”, Biological Cybernetics, Volume 23, Issue 3, pp.121-134, September. [12] Stephen Grossberg, 1976, “Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions”, Biological Cybernetics, Volume 23, Issue 4, pp.187-202, December. [13] Gail A. Carpenter, Stephen Grossberg, 1987, “A massively parallel architecture for a self-organizing neural pattern recognition machine”, Computer Vision, Graphics, and Image Processing, Volume 37, Issue 1, pp.54-115, January. [14] Gail A. Carpenter, Stephen Grossberg, 1988, “The ART of adaptive pattern recognition by a self-organizing neural network”, Computer, Volume 21, Issue 3, pp.77-88, March. [15] Gail A. Carpenter, Stephen Grossberg, 1987, “ART 2: self-organization of stable category recognition codes for analog input patterns”, Applied Optics, Volume 26, Issue 23, pp.4919-4930, December. [16] Gail A. Carpenter, Stephen Grossberg, 1990, “ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures”, Neural Networks, Volume 3, Issue 2, pp.129-152, March. [17] Gail A. Carpenter, Stephen Grossberg, David B. Rosen, 1991, “Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system”, Neural Networks, Volume 4, Issue 6, pp.759-771, June. [18] Gail A. Carpenter, Stephen Grossberg, John H. Reynolds, 1991, “ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network”, Neural Networks, Volume 4, Issue 5, pp.565-588. [19] Gail A. Carpenter, et al., 1992, “Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps”, IEEE Transactions on Neural Networks, Volume 3, Issue 5, pp.698-713, September. [20] M. J. D. Powell, 1985, “Radial basis functions for multivariable interpolation: a review”, IMA Conference on Algorithms for the Approximation of Functions and Data, pp.143-167, New York, July. [21] D. S. Broomhead, David Lowe, 1988, “Multivariable Functional Interpolation and Adaptive Networks”, Complex Systems, Volume 2, Issue 3, pp.321-355. [22] Fred J. Hickernell, Y. C. Hon, 1998, “Radial basis function approximation of the surface wind field from scattered data”, Internat. J. Appl. Sci. Comput., 4, pp.221-247. [23] Kagan Tumer, et al., 1996, “Spectroscopic detection of cervical pre-cancer using radial basis function networks”, United States Patent No. 6135965A. [24] RN Mahanty, PBD Gupta, 2004, “Application of RBF neural network to fault classification and location in transmission lines”, IEE Proceedings - Generation, Transmission and Distribution, Volume 151, Issue 2, pp.201-212, March.
|