Weight control method based on bi-clustering adaptive fuzzy neural network

Abstract

The invention discloses a weight control method based on a bi-clustering adaptive fuzzy neural network, which is characterized in that according to habits of field operators, existing data are utilized to realize weight control under the conditions of no target value record and expert experience. The method comprises the following steps: obtaining a weighing bin weight target value and a fuzzy rule among weighing bin weight deviation, deviation change rate and feeding quantity deviation by utilizing biclustering, further learning the fuzzy rule by utilizing a fuzzy neural network, and finally obtaining a biclustering adaptive fuzzy neural network controller, thereby realizing control on the weight of the weighing bin. According to the invention, the target value of the weight of the weighing bin can be adaptively obtained, and the experience of an operator is learned to obtain the dual-clustering adaptive fuzzy neural network controller, so that the real-time control of the weight of the weighing bin can be realized.

Description

technical field [0001] The invention belongs to the field of weighing warehouse weight control, in particular to a weight control method based on a biclustering adaptive fuzzy neural network. Background technique [0002] In recent years, the intelligent control scheme combining fuzzy control and industrial process has been widely used. However, the fuzzification and fuzzy rules in the fuzzy control process rely on expert experience, which also makes the application of fuzzy control limited. Artificial neural network can simulate the function of human brain, and fit fuzzy rules similar to expert experience through a large amount of data. The combination of artificial neural network and fuzzy control gives birth to fuzzy neural network. Fuzzy neural network can be used as a controller for weighing warehouse weight control. However, the determination of the hidden layer nodes of the fuzzy neural network still depends on the experience of experts. At the same time, for too many...

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Problems solved by technology

[0002] In recent years, the intelligent control scheme combining fuzzy control and industrial process has been applied more and more. However, the fuzzification and fuzzy rules in the fuzzy control process rely on expert experience, which also limits the application of fuzzy control.

The artificial neural network can simulate the function of the human brain, and fit fuzzy rules similar to expert experience through a large amount of data. The combination of artificial neural network and fuzzy control creates a fuzzy neural network. The fuzzy neural network can be used as a controller to control the weight of the weighing bin. However, the determination of the hidden layer nodes of the fuzzy neural network still depends on expert experience. At the same time, for too many hidden layer nodes, the network learning time will be too long, and too few hidden layer nodes will not be able to correctly and fully reflect the operation At the same time, the weight control of weighing bins in most cement plants is still controlled by manual experience. Different operators have different expectations for weighing bin weights, and there is no real-time weighing bin weight target value record.

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