초록 ▼

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 retrievi

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.

대표청구항 ▼

A method for constructing training sets for a neural network, comprising the steps of: (1) developing a first training set for a neural network by: (a) retrieving from an historical database first training input data having a first timestamp(s); (b) selecting a first training input data time based o

A method for constructing training sets for a neural network, comprising the steps of: (1) developing a first training set for a neural network by: (a) retrieving from an historical database first training input data having a first timestamp(s); (b) selecting a first training input data time based on said first timestamp(s); (c) retrieving a first input data indicated by said first training input data time; and (2) developing a second training set for said neural network by: (a) retrieving from said historical database second training input data having a second timestamp(s); (b) selecting a second training input data time based on said second timestamp(s); (c) retrieving a second input data indicated by said second training input data time.