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D-FNN direct inverse control method and system based on pruning policy

A control system and inverse control technology, applied in general control systems, control/regulation systems, adaptive control, etc., can solve problems such as inability to process and describe fuzzy information, difficulty in realizing adaptive learning, etc.

Inactive Publication Date: 2019-10-22
FOSHAN UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The neural network simulates the structure of the human brain and has large-scale parallel and distributed information processing capabilities, but it cannot process and describe fuzzy information
The fuzzy system has a reasoning process that is easy to understand, but it is difficult to realize the function of adaptive learning

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  • D-FNN direct inverse control method and system based on pruning policy
  • D-FNN direct inverse control method and system based on pruning policy
  • D-FNN direct inverse control method and system based on pruning policy

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Embodiment Construction

[0105] The concept, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and drawings, so as to fully understand the purpose, scheme and effect of the present disclosure. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0106] Such as figure 1 Shown is a flow chart of a D-FNN direct inverse control method based on a pruning strategy according to the present disclosure, combined below figure 1 A pruning strategy-based D-FNN direct inverse control method according to an embodiment of the present disclosure will be described.

[0107] The disclosure proposes a D-FNN direct inverse control method based on a pruning strategy, which specifically includes the following steps:

[0108] Step 1, read the input data set;

[0109] Step 2, constructing a dynamic fuzzy neural ...

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Abstract

The invention discloses a D-FNN direct inverse control method and a D-FNN direct inverse control system based on a pruning policy. The D-FNN direct inverse control method can ensure a more concise structure and shorter learning time by adopting classified learning. The classified learning strategy has a great advantage of being capable of alleviating an oscillation problem in the learning process,and the D-FNN has high adaptability and robustness and can reduce trajectory errors to near zero quickly when disturbance occurs in an external environment. The D-FNN can automatically generate or delete a fuzzy rule according to the importance of the inverse control system and the complexity of the system, does not need to preset a model during online learning, can set an order for adaptive learning based on the training data, thereby compensating for the non-linear system modeling error in processing external disturbance. Through simulation research, the dynamic fuzzy neural network can beapplied to many real-time automatic control systems.

Description

technical field [0001] The present disclosure relates to the technical fields of automatic control, artificial intelligence and neural network, in particular to a D-FNN direct inverse control method and system based on a pruning strategy. Background technique [0002] A well-trained neural network can be regarded as an expression of knowledge. Unlike fuzzy systems that use IF-THEN rules to express local knowledge, neural networks pass through its structure, more specifically, through its connection weights and local processing Units store knowledge in a distributed or local way. Feedforward computation in neural networks plays the same role as forward reasoning in fuzzy systems. These two systems can perform tasks according to the current situation by operating on the stored knowledge, and the expected output has been obtained. Responding to new situations by giving an appropriate behavior is the core of these two systems. However, the way the two complete the task is diff...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 张彩霞王向东王新东曾平
Owner FOSHAN UNIVERSITY
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