Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté. Image fixe : sans médiation
Auteur(s) : Graupe, Daniel
Titre(s) : Principles of artificial neural networks [Texte imprimé] / Daniel Graupe,...
Édition : 3rd ed.
Publication : New Jersey : World scientific, cop. 2013
Description matérielle : 1 vol. (XVIII-363 p.) : ill. ; 26 cm
Collection : Advanced series in circuits and systems ; volume 7
Lien à la collection : Advanced series in circuits and systems
Comprend : Ch. 1. Introduction and role of artificial neural networks ; ch. 2. Fundamentals
of biological neural networks ; ch. 3. Basic principles of ANNs and their early structures.
3.1. Basic principles of ANN design. 3.2. Basic network structures. 3.3. The Perceptron's
input-output principles. 3.4. The Adaline (ALC) ; ch. 4. The Perceptron. 4.1. The
basic structure. 4.2. The single-layer representation problem. 4.3. The limitations
of the single-layer Perceptron. 4.4. Many-layer Perceptrons. 4.A. Perceptron case
study: identifying autoregressive parameters of a signal (AR time series identification)
; ch. 5. The Madaline. 5.1. Madaline training. 5.A. Madaline case study: character
recognition ; ch. 6. Back propagation. 6.1. The back propagation learning procedure.
6.2. Derivation of the BP algorithm. 6.3. Modified BP algorithms. 6.A. Back propagation
case study: character recognition. 6.B. Back propagation case study: the exclusive-OR
(XOR) problem (2-layer BP). 6.C. Back propagation case study: the XOR problem ; 3
layer BP network. 6.D. Average monthly high and low temperature prediction using backpropagation
neural networks ; ch. 7. Hopfield networks. 7.1. Introduction. 7.2. Binary Hopfield
networks. 7.3. Setting of weights in Hopfield nets ; bidirectional associative memory
(BAM) principle. 7.4. Walsh functions. 7.5. Network stability. 7.6. Summary of the
procedure for implementing the Hopfield network. 7.7. Continuous Hopfield models.
7.8. The continuous energy (Lyapunov) function. 7.A. Hopfield network case study:
character recognition. 7.B. Hopfield network case study: traveling salesman problem.
7.C. Cell shape detection using neural networks.
Ch. 8. Counter propagation. 8.1. Introduction. 8.2. Kohonen self-organizing map (SOM)
layer. 8.3. Grossberg layer. 8.4. Training of the Kohonen layer. 8.5. Training of
Grossberg layers. 8.6. The combined counter propagation network. 8.A. Counter propagation
network case study: character recognition ; Ch. 9. Large scale memory storage and
retrieval (LAMSTAR) network. 9.1. Motivation. 9.2. Basic principles of the LAMSTAR
neural network. 9.3. Detailed outline of the LAMSTAR network. 9.4. Forgetting feature.
9.5. Training vs. operational runs. 9.6. Operation in face of missing data. 9.7. Advanced
data analysis capabilities. 9.8. Modified version: normalized weights. 9.9. Concluding
comments and discussion of applicability. 9.A. LAMSTAR network case study: character
recognition. 9.B. Application to medical diagnosis problems. 9.C. Predicting price
movement in market microstructure via LAMSTAR. 9.D. Constellation recognition.
Note(s) : Bibliogr. p. 349-356
Sujet(s) : Réseaux neuronaux (informatique)
Indice(s) Dewey :
006.32 (23e éd.) = Réseaux neuronaux (informatique)
Identifiants, prix et caractéristiques : ISBN 9789814522731. - ISBN 9814522732 (rel.)
Identifiant de la notice : ark:/12148/cb443901418
Notice n° :
FRBNF44390141
(notice reprise d'un réservoir extérieur)
