Neural network frames. Application to biochemical kinetic diagnosis

Neural network frames. Application to biochemical kinetic diagnosis

Source :

• Computers and Chemical Engineering [ 0098-1354 ] ; 1993.

Descripteurs français

• Pascal (Inist)
  • Cinétique, Fermentation, Inhibition, Modèle, Méthode étude, Réseau neuronal.
• Wicri :
  • topic : Biomasse, Classification, Simulation.

English descriptors

• KwdEn :
  • Aberrant case, Academic press, Adaptive, Alarm state, Algorithm, Annealing methods, Biomass, Biomass concentration, Black pixel, Cbio, Cbio cbio, Classification, Classification algorithm, Classification index, Classification process, Coefficient, Complexity level, Complexity levels, Composition rule, Different classifications, Different conditioning levels, Different levels, Different paths, Different types, Dilution rate, Distance matrices, Distances results, Equal weight coefficients, Ergodic behaviour, Experimental data, Expert system, Fermentation, First step, General frame, Genetic algorithms, Good training example, Hierarchical, Hierarchical order, Hierarchical organization, Infinite memory, Information theory, Informational criteria, Informational type, Inhibition, Input layer, Investigation method, Iordache, Kinetic, Kinetic data, Kinetic diagnosis, Kinetic phenomenon, Kinetic regime, Kinetic regimes, Kinetics, Knowledge representation, Lactic acid production, Matrix, Minimum distance, Modeling method, Models, Neural, Neural network, Neural network frames, Neural network frames table, Neural networks, Neuron, Next cycle, Next level, Numerical experiments, Numerical information, Output functions, Pattern recognition, Permutation, Pixel, Polystochastic, Polystochastic frame, Polystochastic models, Possible conditions, Random iterations, Reference classification, Reference matrix, Residence time, Residence times, Same classification, Simulation, Stable matrix, Stochastic, Stochastic approximation procedures, Stochastic chains, Training data, Transition probabilities, Untrained data, Weight coefficient, Weight coefficients, White pixel.
• Teeft :
  • Aberrant case, Academic press, Adaptive, Alarm state, Algorithm, Annealing methods, Biomass, Biomass concentration, Black pixel, Cbio, Cbio cbio, Classification, Classification algorithm, Classification index, Classification process, Coefficient, Complexity level, Complexity levels, Composition rule, Different classifications, Different conditioning levels, Different levels, Different paths, Different types, Dilution rate, Distance matrices, Distances results, Equal weight coefficients, Ergodic behaviour, Experimental data, Expert system, First step, General frame, Genetic algorithms, Good training example, Hierarchical, Hierarchical order, Hierarchical organization, Infinite memory, Information theory, Informational criteria, Informational type, Input layer, Iordache, Kinetic, Kinetic data, Kinetic diagnosis, Kinetic phenomenon, Kinetic regime, Kinetic regimes, Kinetics, Knowledge representation, Lactic acid production, Matrix, Minimum distance, Modeling method, Neural, Neural network, Neural network frames, Neural network frames table, Neural networks, Neuron, Next cycle, Next level, Numerical experiments, Numerical information, Output functions, Pattern recognition, Permutation, Pixel, Polystochastic, Polystochastic frame, Polystochastic models, Possible conditions, Random iterations, Reference classification, Reference matrix, Residence time, Residence times, Same classification, Simulation, Stable matrix, Stochastic, Stochastic approximation procedures, Stochastic chains, Training data, Transition probabilities, Untrained data, Weight coefficient, Weight coefficients, White pixel.

Abstract

• https://api.istex.fr/document/0FA91326A62B55830D3C6B1762335C017EFFB795/fulltext/pdf

Affiliations:

• France, Mexique
• Grand Est, Lorraine (région)
• Nancy
• Centre national de la recherche scientifique, Laboratoire réactions et génie des procédés, Université de Lorraine

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