. Critère, Ratio 110 < 3 mois ? Rotation du besoin en fonds de roulement (en mois) (ratio 115) Analyse de l'expert : Domaine de

. Critère, Ratio 115 < 3 mois ? ? Investissements / Valeur ajoutée (ratio 145) Ce ratio permet d'apprécier la politique d'investissements suivie par l'entreprise. L'expert ne définit ni un domaine de validité

D. Annexe, Neural-Network-Aided Portfolio Management #!

A. D. Neural-network-aided, . Portfolio, and . Management, Cet article a été publié dans : Industrial application of neural networks, 1996.

E. Annexe, A New Decision Criterion for Feature Selection $

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

E. I. Altman, Corporate Financial Distress and Bankruptcy
DOI : 10.1002/9781118267806

A. Antoniadis, J. Berruyer-&-r, and . Carmona, Régression non linéaire et applications

R. E. Bellman, Adaptive Control Processes : A Guided Tour
DOI : 10.1515/9781400874668

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

A. Björck, Solving linear least squares problems by Gram-Schmidt orthogonalization, BIT, vol.30, issue.1
DOI : 10.1007/BF01934122

J. Bouinot, La nouvelle gestion municipale : Comptabilité et management d'une commune

H. Bourlard-&-n and . Morgan, Connectionist Speech Recognition : A Hybrid Approach
DOI : 10.1007/978-1-4615-3210-1

C. G. Broyden, The Convergence of a Class of Double-rank Minimization Algorithms, IMA Journal of Applied Mathematics, vol.6, issue.3, pp.222-231, 1970.
DOI : 10.1093/imamat/6.3.222

B. C. Cetin, J. W. Barhen-&-j, and . Burdick, Terminal Repeller Unconstrained Subenergy Tunnelling (TRUST) for Fast Global Optimization
DOI : 10.1007/bf00940781

S. Chen, S. A. Billings-&-w, and . Luo, Orthogonal least squares methods and their application to non-linear system identification, International Journal of Control, vol.10, issue.5, pp.1873-1896, 1989.
DOI : 10.2307/2284566

T. Cibas, F. Fogelman-soulié, P. Gallinari-&-s, and . Raudys, Variable selection with neural networks, Neurocomputing, vol.12, issue.2-3, pp.223-248, 1996.
DOI : 10.1016/0925-2312(95)00121-2

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.357

G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems, vol.27, issue.4, pp.303-314, 1989.
DOI : 10.1007/BF02551274

G. Dreyfus, L. Personnaz, and G. , TOULOUSE "Perceptrons, Past and Present, Enciclopedia Italiana

R. O. Duda-&-p and . Hart, Pattern classification and scene analysis

A. F. Duprat, T. Huynh-&-g, and . Dreyfus, Parsimonious, Accurate Neural Network Prediction of LogP

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2
DOI : 10.1111/j.1469-1809.1936.tb02137.x

R. Fletcher, A new approach to variable metric algorithms, The Computer Journal, vol.13, issue.3
DOI : 10.1093/comjnl/13.3.317

D. T. Fourdrinier-&-m and . Wells, Comparaisons de procédures de sélection d'un modèle de régression : une approche décisionnelle

K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks, vol.2, issue.3, pp.183-192, 1989.
DOI : 10.1016/0893-6080(89)90003-8

P. Gallinari, S. Thiria, F. Badran-&-f, and . Fogelman-soulie, On the relations between discriminant analysis and multilayer perceptrons, Neural Networks, vol.4, issue.3, pp.349-360, 1991.
DOI : 10.1016/0893-6080(91)90071-C

D. Goldfarb, A family of variable-metric methods derived by variational means, Mathematics of Computation, vol.24, issue.109, pp.23-26, 1970.
DOI : 10.1090/S0025-5718-1970-0258249-6

G. C. Goodwin-&-r and . Payne, Dynamic System Identification : Experiment Design and Data Analysis, Mathematics in Science and Engineering, vol.136, 1977.

A. Gremillet, Les ratios et leur utilisation

B. G. Hassibi-&-d and . Stork, Second order derivatives for network pruning : optimal brain surgeon

K. Hornik, M. Stinchcombe, H. White-&-p, and . Auer, Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their Derivatives, Neural Computation, vol.6, issue.6, pp.1262-1275, 1992.
DOI : 10.1016/0893-6080(94)90063-9

I. De and K. , La comptabilité analytique de la commune, Éditions du Moniteur, 1992.

M. Klopfer, La guide de la gestion financière : Endettement, trésorerie et solvabilité des collectivités locales

S. Knerr, Réseaux de neurones pour la classification automatique ; application à la reconnaissance de chiffres manuscrits

V. Koroliouk, N. Portenko, A. Skorokhod-&-a, and . Mir, Aide-mémoire de théorie des probabilités et de statistique mathématique, 1983.

J. De and L. , Initiation à l'analyse des données

I. J. Leontaritis-&-s and . Billings, Model selection and validation methods for non-linear systems, International Journal of Control, vol.2, issue.1, pp.311-341, 1987.
DOI : 10.1080/00207177708922285

P. Leray-&-p and . Gallinari, Report on variable selection

K. Levenberg, A method for the solution of certain non-linear problems in least squares, Quarterly of Applied Mathematics, vol.2, issue.2
DOI : 10.1090/qam/10666

D. W. Marquardt, An algorithm for least-squares estimation of non-linear parameters

M. Minoux, Programmation mathématique, théorie et algorithmes

C. Monrocq, Approche probabiliste pour l'élaboration et la validation de systèmes de décision : Application aux réseaux de neurones

J. C. Nash, Compact Numerical Methods for Computers : linear algebra and function minimisation

O. Nerrand, P. Rousel-ragot, L. Personnaz, G. Dreyfus-&-s, and . Marcos, Neural Networks and Non-Linear Adaptive Filtering : Unifying Concepts and New Algorithms, Neural Computation, vol.5, pp.2-165, 1993.

O. Nerrand, P. Rousel-ragot, D. Urbani, L. Personnaz-&-g, and . Dreyfus, Training recurrent neural networks: why and how? An illustration in dynamical process modeling, IEEE Transactions on Neural Networks, vol.5, issue.2, pp.2-178, 1994.
DOI : 10.1109/72.279183

J. P. Norton, An introduction to Identification, 1986.

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, issue.3
DOI : 10.1214/aoms/1177704472

M. J. Powell, Some global convergence properties of a variable metric algorithm for minimization without exact line searches

W. H. Press, S. A. Teukolsky, W. T. Vetterling-&-b, and . Flannery, Numerical Recipies in C : The Art of Scientific Computing, 1992.

D. Price, Classification probabiliste par réseaux de neurones ; application à la reconnaissance de l'écriture manuscrite

P. Refregier, A. Jaffre-&-f, and . Vallet, Une approche probabiliste pour les problèmes de discrimination entre plusieurs classes par réseaux neuronaux

M. D. Richard-&-r and . Lippmann, Probabilities, Neural Computation, vol.2, issue.11, pp.461-483, 1991.
DOI : 10.1162/neco.1989.1.4.425

I. Rivals, Modélisation et commande de processus par réseaux de neurones

R. Rojas, A Short Proof of the Posterior Probability Property of Classifier Neural Networks, Neural Computation, vol.3, issue.4, pp.41-43, 1996.
DOI : 10.1162/neco.1996.8.1.41

D. E. Rumelhart, G. E. Hinton-&-r, and . Williams, Learning Internal Representations by Error Propagation" Parallel Distributed Processing, pp.318-362, 1986.

G. Saporta, Probabilités, analyse des données et statistique, Editions Technip, 1990.

D. F. Shanno, Conditioning of quasi-Newton methods for function minimization, Mathematics of Computation, vol.24, issue.111, pp.641-656, 1970.
DOI : 10.1090/S0025-5718-1970-0274029-X

P. Siarry-&-g and . Dreyfus, La méthode du recuit simulé" IDSET, 1988.

T. Söderström, On model structure testing in system identification, International Journal of Control, vol.26, issue.1, pp.1-18, 1977.
DOI : 10.1016/0005-1098(74)90034-X

B. Solnik, Gestion Financière" Editions Fernand Nathan, 1980.

E. D. Sontag, Neural Networks for Control Essays on Control : Perspectives in the Theory and its Applications

D. Urbani, Méthodes statistiques de sélection d'architectures neuronales : application à la conception de modèles dynamiques

P. Vernimmen, Finance d'entreprise : analyse et gestion

P. Wolfe, Convergence Conditions for Ascent Methods, SIAM Review, vol.11, issue.2
DOI : 10.1137/1011036