Further Reading

Almeida LB (1987) A learning rule for asynchronous perceptrons with feedback in a combinatorial environment. Proceedings, IEEE International Conference on Neural Networks, vol. II, pp. 609-618. San Diego: IEEE Press. Angelovic P (2005) Time series prediction using RSOM and local models. In: IIT. SRC 2005. Bratislava, Slovakia: Slovak University of Technology.

Barreto G, AraUjo A, and Kremer SC (2003) A taxonomy for spatiotemporal connectionist networks revisited: The unsupervised case. Neural Computation 15(6): 1255-1320. Chappell GJ and Taylor JG (1993) The temporal Kohonen map. Neural

Networks 6: 441-445. Chon T-S, Kwak I-S, Park Y-S, Kim T-H, and Kim YS (2001) Patterning and short-term predictions of benthic macroinvertebrate community dynamics by using a recurrent artificial neural network. Ecological Modelling 146: 181-193.

Chon T-S, Park Y-S, and Cha EY (2000) Patterning of community changes in benthic macroinvertebrates collected from urbanized streams for the short time prediction by temporal artificial neuronal networks. In: Lek S and Guegan JF (eds.) Artifical Neuronal Networks, pp. 99-114. Berlin: Springer.

Elman JL (1990) Finding structure in time. Cognitive Science 14: 179-211.

Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America 79: 2554-2558.

Jordan MI (1986) Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, pp. 531-546. Hillsdale, NJ: Lawrence Erlbaum.

Kohonen T (2001) Self-Organizing Maps, 3rd edn. Berlin: Springer.

Koskela T, Varsta M, Heikkonen J, and Kaski K (1998) Temporal sequence processing using recurrent SOM. In: Proceedings of 2nd International Conference on Knowledge-Based Intelligent Engineering Systems, vol. 1, pp. 290-297.

Kosko B (1987) Adaptive bidirectional associative memories. Applied Optics 26(23): 4947-4959.

Kung SY (1993) Digital Neural Networks. Englewood Cliffs, NJ: Prentice Hall.

Lin C-T and Lee CSG (1996) Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice Hall.

Pineda FJ (1987) Generalization of back-propagation to recurrent neural networks. Physical Review Letters 59(19): 2229-2232.

Son K-H, Ji CW, ParkY-M, etal. (2006) Recurrent Self-Organizing Map implemented to detection of temporal line-movement patterns of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) in response to the treatments of heavy metal. In: Kungolos AG, Brebbia CA, Samaras CP, Popov V (eds.) Environmental Toxicology. WIT Transaction on Biomedicine and Health vol. 10, pp. 77-91. Southampton, UK: WIT press.

Lek S and Guegan J-F (2000) Artificial Neuronal Networks, 248pp. Heidelberg: Springer.

Varsta M, Heikkonen J, and Millan J del R (1997) Context learning with the self-organizing map. In: Proceedings ofWSOM, pp. 197-202. Helsinki: Helsinki University of Technology.

Williams RJ and Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1: 270-280.

10 Ways To Fight Off Cancer

10 Ways To Fight Off Cancer

Learning About 10 Ways Fight Off Cancer Can Have Amazing Benefits For Your Life The Best Tips On How To Keep This Killer At Bay Discovering that you or a loved one has cancer can be utterly terrifying. All the same, once you comprehend the causes of cancer and learn how to reverse those causes, you or your loved one may have more than a fighting chance of beating out cancer.

Get My Free Ebook


Post a comment