By Giacomo Indiveri (auth.), Roberto Tagliaferri, Maria Marinaro (eds.)

ISBN-10: 1447102193

ISBN-13: 9781447102199

ISBN-10: 185233505X

ISBN-13: 9781852335052

This booklet comprises the lawsuits of the twelfth Italian workshop on neural nets which was once held in Salerno, Italy from 17-19 may well 2001. Bringing jointly some of the best study and improvement from the clinical neighborhood, it presents in-depth analyses of issues within the components of Architectures and Algorithms, photograph and sign Processing, and Applications.
Of specific curiosity are the following:
Invited lectures on: Computation in Neuromorphic Analog VLSI structures; On Connectionism and Rule Extraction; and past basic Rule Extraction: buying making plans wisdom from Neural Networks;
A assessment speak on: Neurofuzzy Approximator in response to Mamdani's Model;
Papers from a different consultation on: From Synapses to Rules.
This quantity presents a state of the art review of present learn and may be of curiosity to graduate and postgraduate scholars and researchers within the fields of desktop technology; engineering; physics and arithmetic.

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Additional info for Neural Nets WIRN Vietri-01: Proceedings of the 12th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 17–19 May 2001

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Vol. 15, 1985, pp. 116-132. lM. Mendel, "Fuzzy logic systems for engineering: a tutorial", Proc. IEEE, Vol. 3, 1995, pp. 345-377. X. Wang, Adaptive fuzzy systems and control, Prentice-Hall, Englewood Cliffs, NJ, 1994. R. T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing, Prentice-Hall, NJ, USA, 1997. A. Carpenter, S. , "Fuzzy ARTMAP: a neural network architecture for incremental supervised learning and analog multidimensional maps", IEEE Trans. on Neural Networks, Vol. 3, 1992, pp. 698-713.

LC. Bezdeck, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, 1981. K. Simpson, "Fuzzy min-max neural networks-Part. 2: clustering", IEEE Trans. , Vol. 1, 1993, pp. 32-45. M. Frattale Mascioli, A. Mancini, A. Rizzi, and G. Martinelli, "Function approximation with noisy training data using FBF neural networks", Proc. of NC'98, Vienna, Sept. 1998, pp. 900-906. VN. Vapnik, The nature of statistical learning theory, Springer-Verlag, 1995. J. Rissanen, ''Modelling by shortest data description", Automatica, Vol.

An optimal procedure for carrying out the splitting operation exists only in the asymptotic case [16]. Since in actual applications the training set cardinality is far from this situation, it is necessary to follow a rule of thumb, as it will be done in Sect. 6. e. the final architecture of the net is obtained starting from a minimal structure and then increasing step-by-step its size. The constructive approach is important not only for improving generalization but also for combating noise. The presence of noise reduces the information content of the numerical data and apparently renders more complex the model to be fitted by the net.

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Neural Nets WIRN Vietri-01: Proceedings of the 12th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 17–19 May 2001 by Giacomo Indiveri (auth.), Roberto Tagliaferri, Maria Marinaro (eds.)


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