Learning in Lattice neural networks that employ dendritic computing

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2006, Pages 7-13

  Ritter, Gerhard X ^a,b   Schmalz, Mark S ^a,b  

^a IEEE   (United States)

^b University of Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL ELEMENTS; LATTICE NEURAL NETWORKS; LOGIC FUNCTIONS; NEUROSCIENTIST;

FORMAL LOGIC; LEARNING ALGORITHMS; NEURAL NETWORKS;

LEARNING SYSTEMS;

EID: 34250765232     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2006.1681687     Document Type: Conference Paper

References (22)

1. J. M. Keller and D. J. Hunt, "Incorporating fuzzy membership functions into the perceptron algorithm," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 7, pp. 693-699, Nov. 1985.
2. R. J. Marks, S. Oh, P. Arabshahi, T. P. Caudell, J. J. Choi, and B. G. Song, "Steepest descent adaption of min-max fuzzy if-then rules," Proc. Proc. IJCNN, Beijing, China, vol. III, 1992, pp. 471-477.
3. P. K. Simpson, "Fuzzy min-max neural networks - Part 1: Classification," IEEE Transaction on Neural Networks, vol. 3, pp. 776-786, Sept. 1992.
4. P. K. Simpson, "Fuzzy min-max neural networks - Part 2: Clustering," IEEE Trans. on Fuzzy Systems, vol. 1, pp. 32-45, 1993.
5. X. H. Zhang, F. Wong, and P. Z. Wang, "Decision-support neural networks and its application in financial forecast," Bull. Studies and Exchanges on Fuzzyness Applications, no. 54, pp. 40-49, 1993.
6. X. H. Zhang, C. Hang, S. Tan, and P. Z. Wang, "The min-max function differentiation and training of fuzzy neural networks," IEEE Transactions on Neural Networks, vol. 7, pp. 1139-1150, Sept. 1996.
7. G. X. Ritter and P. Sussner, "Associative memories based on lattice algebra," Proc. IEEE Int. Conf. Syst., Man and Cybern., Orlando, FL., 1997, pp. 3570-3575.
8. Y. Won, P. D. Gader, and P. Coffield, "Morphological shared-weight neural networks with applications to automatic target recognition," IEEE Transactions on Neural Networks, vol. 8, pp. 1195-1203, Sept. 1997.
9. V. Petridis and V. G. Kaburlasos, "Fuzzy neural networks (FLNN): A hybrid model for learning," IEEE Transactions on Neural Networks, vol. 9, pp. 877-890, Sept. 1998.
10. G. X. Ritter, P. Sussner, and J. L. Diaz de Leon, "Morphological associative memories," IEEE Transactions on Neural Networks, vol. 9, pp. 281-293, Mar. 1998.
11. G. X. Ritter, J. L. Diaz de Leon, and P. Sussner, "Morphological bidirectional associative memories," Neural Networks, vol. 12, pp. 851-867, Mar. 1999.
12. G. X. Ritter, G. Urcid, and L. Iancu, "Reconstruction of noisy patterns using morphological associative memories," J. Math. Imag. Vis., vol. 19, pp. 95-111, Sept. 2003.
13. V. G. Kaburlasos, "Improved fuzzy lattice neurocomputing (FLN) for semantic neural computing," Proc. Proc. Intern. Joint Conf. on Neural Networks (IJCNN), Portland, OR, 2003, pp. 1850-1855.
14. C. Koch and I. Segev, Methods in Neuronal Modeling: From Synapses to Networks, Camridge, MA: MIT Press, 1989.
15. W. Rail and I. Segev, "Functional possibilities for synapses on dendrites and dendritic spines," in Synoptic Function. Edited by G. M. Edelman, E. E. Gall, and W. M. Cowan, New York, NY: Wiley, 1987.
16. I. Segev, "Dendritic processing," in Handbook of Brain Theory and Neural Networks. Edited by M. Arbib, Cambridge, MA: MIT Press, 1998.
17. T. J. Sejnewski and N. Qian, "Synaptic integration by electro-diffusion in dendritic spines," in Single Neuron Computation. Edited by T. McKenna, J. Davis, and S. F. Zornetzer, New York, NY: Academic Press, 1992.
18. J. C. Eccles, The Understanding of the Brain, New York, NY: McGraw Hill, 1977.
19. G. X. Ritter and G. Urcid, "Lattice algebra approach to single neuron computation," IEEE Trans. on Neural Networks, 14(2)(2003), pp. 282-295.
20. G. X. Ritter, L. Iancu L, and G. Urcid, "Morphological perceptrons with dendritic structure," Proc. FUZZ-IEEE '03, St. Louis, MO, 2003, pp. 1296-1301.
21. G. X. Ritter and L. Iancu, "Single layer feedforward neural network based on lattice algebra," Proc. Proc. of Intern. Joint Conf. on Neural Networks (IJCNN), Portland, OR, 2003, pp. 2887-2892.
22. V. A. Wasnikar and A. D. Kulkarni, "Data Mining with Radial Basis Functions," in In: Dagli CH, et al. (eds) Intelligent Engineering Systems Through Artificial Neural Networks, edited by C. H. Dagli, et al., New York: ASME Press, 2000.