Patent
1995-04-17
1997-06-17
Hafiz, Tariq R.
395 21, 395 24, G06E 100, G06E 300, G06F 1618
Patent
active
056404930
ABSTRACT:
An on-line training neural network for controlling a process for producing a product having at least one product property that trains by retrieving training sets from a stream of process data. The neural network detects the availability of new training data, and constructs a training set by retrieving the corresponding input data. The neural network is trained using the training set. Over time, many training sets are presented to the neural network. When multiple presentations are needed to effectively train, a buffer of training sets is filled and updated as new training data becomes available. The size of the buffer is selected in accordance with the training needs of the neural network. Once the buffer is full, a new training set bumps the oldest training set off the top of the buffer stack. The training sets in the buffer stack can be presented one or more times each time a new training set is constructed. An historical database of timestamped data can be used to construct training sets when training input data has a time delay from sample time to availability for the neural network. The timestamps of the training input data are used to select input data for use in the training set. Using the historical database, the neural network can be trained retrospectively by searching the historical database and constructing training sets based on past data.
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E. I. Du Pont de Nemours & Co., (Inc.)
Hafiz Tariq R.
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