Neural Network Modelling of Fermentation Taking into Account Culture Memory

1997

Bioprocess Engineering

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In the present paper the problem of chemostat modelling using the neural networks techniques is considered. A feedforward neural network with time delay feedback connections from and to the output neurons, which take into account the culture memory is proposed. A model of the growth of a strain Saccharomyces cerevisiae on a glucose limited medium is developed. Simulation investigations are carried out. The results are discussed.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural network modelling of fermentation processes. Microorganisms cultivation model

1997

Bioprocess Engineering

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“…12 In this way, the present output of the neural network depends on its previous values. These delay elements can be connected to the hidden and/or output neurons of the main neural network.…”

Section: Introductionmentioning

confidence: 99%

Memory effects description by neural networks with delayed feedback connections

2004

Int. J. Intell. Syst.

For the purpose of dynamic systems modeling, it was proposed to include feedback connections or delay elements in the classical feed-forward neural network structure so that the present output of the neural network depends on its previous values. These delay elements can be connected to the hidden and/or output neurons of the main neural network. Each delay element gets a value of a state variable at a past time instant and keeps this value during a single sampling period. The groups of delay elements record the values of the state variables for a given time period in the past. Changing the number of the delay elements, which belongs to one group, a shorter or a longer time period in the past can be accounted for. Thus, the connection weights determine the influence of the past process states on the present state in a similar way as it is in the time delay kernel or cause-effect relation membership function (CER-MF) models. Specific feed-forward neural networks with time delay connections are used to solve the problem of neural network chemostat modeling as well as specific kinetic rates modeling. The weights of the feedback connections obtained during model training are discussed as the points of a time delay kernel or as the strength levels in a CER model (the points in the CER-MF). The corresponding changes in these weights with the changing time period in the past are shown.

“…There are numerous examples of NN applications in modelling and control of fermentation processes [4,5,6,7,8]. Two approaches for neural network modelling of chemostat both accounting for the culture memory were advanced recently [7].…”

mentioning

confidence: 99%

“…Two approaches for neural network modelling of chemostat both accounting for the culture memory were advanced recently [7]. The ®rst approach uses a feedforward NN with delay elements from and to the output neurons.…”

mentioning

confidence: 99%

Neural network modelling of fermentation processes

et al. 1998

Bioprocess Engineering

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This paper deals with an application of neural networks for chemostat modelling. A feedforward neural network, taking into account culture memory is proposed for the speci®c growth rate approximation within the framework of the classical unstructured model. The investigations are carried out for different network topologies on the example of the growth of a strain Saccharomyces cerevisiae on a glucose limited medium and a model suitable for control synthesis is proposed.