Ink key opening forecasting method having increment type learning capacity

Abstract

The invention relates to a method for presetting digital printing ink and provides an ink key opening forecasting method based on a Fuzzy ART-BP mixed neural network algorithm of a Fuzzy ART neural network and a BP neural network. An input vector is stably classified by fully utilizing the self-learning, self-organizing and information fuzzy processing capacities of the Fuzzy ART neural network by the network; for each classification, nonlinear mapping is performed on the input and output vectors of a training sample by utilizing the BP neural network, namely, a mapping relation between image-text digital information and ink key control parameter of the training sample is established by taking printing on-site temperature, humidity, a printer rotating speed and a website area rate corresponding to an ink area as input vectors and an ink key opening value as an output vector; and a converged network is used for forecasting the ink key opening value of a new sample. The network learning is higher in pertinence, the iteration times of the BP network is reduced, the network has the increment type learning capacity and the generalization of the network is increased.

Description

technical field [0001] The invention belongs to the field of digital printing, and in particular relates to an ink key opening prediction method with incremental learning ability. Background technique [0002] With the increasing use of digital files in the prepress field, digital processes are increasingly used in printing processes, and the role of digital processes in CTP (computer-to-plate) technology is becoming more and more important. At the same time, printing companies are facing more and more short boards, complex and fast printing production activities, which also put forward higher requirements for printing companies. For printing companies, an effective way to shorten the makeready time is to pre-set the ink. Pre-estimating the optimal ink output of the ink tank and presetting the ink keys can save a lot of printing machine start-up preparation time, reduce production costs, improve printing quality and efficiency, and significantly reduce paper waste. [0003...

AI Technical Summary

Problems solved by technology

[0004] The traditional ink preset technology based on BP neural network algorithm does not have the incremental learning ability (online learning) of training sample data, and the generalization ability is weak. When training and learning new sample data, it will destroy the model that the network has memorized, resulting in The ink key opening prediction result of the network is not accurate enough

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