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BP neural network algorithm based on PID adjustment

A BP neural network and algorithm technology, applied in the field of BP neural network algorithm based on PID adjustment, can solve problems such as convergence dependence on initialization, and achieve the effects of fast gradient update, reduced randomness, and accelerated convergence

Pending Publication Date: 2022-03-01
河南驼人医疗器械研究院有限公司
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Problems solved by technology

However, the existing neural network algorithm PID self-tuning converges occasionally and randomly, and the convergence is very dependent on initialization, requiring specific parameters for initialization

Method used

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  • BP neural network algorithm based on PID adjustment
  • BP neural network algorithm based on PID adjustment
  • BP neural network algorithm based on PID adjustment

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Embodiment Construction

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0028] Such as figure 1 , figure 2 , image 3 Shown, the present invention specifically relates to the BP neural network algorithm based on PID regulation, comprises the following steps:

[0029] Step 1. Initialize the BP neural network: set the input layer points X and the hidden layer points H of the BP neural network, and give the initial weight W of the hidden layer HI and the initial weight W of the output layer HO；

[0030] Step 2: Sampling to obtain the actual temperature ...

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Abstract

The invention discloses a BP (Back Propagation) neural network algorithm based on PID (Proportion Integration Differentiation) adjustment, which obtains a parameter self-adaptive effect by continuously adjusting three parameters of proportion (P), integral (I) and differential (D) through an incremental neural network PID controller, and through a Momentum gradient updating algorithm, the BP neural network algorithm based on PID adjustment is enabled to be faster and more stable in temperature control data convergence and no divergence phenomenon.

Description

technical field [0001] The invention specifically relates to a BP neural network algorithm based on PID regulation. Background technique [0002] PID control is a control method that is widely used in the production process. The process control is carried out through three parameters of the deviation ratio (P), integral (I) and differential (D). The settings of the three PID parameters have a deep impact on the system. This requires experienced professionals to continuously adjust the system to find the three parameters that make the system close to the optimal working state; thus a method of optimally controlling the adaptive PID control system is produced; in this control The system is required to be able to automatically adjust the system according to changes in the measured parameters, the environment and the cost of raw materials, so that it can be in the best state at any time. [0003] Common minimum variance adaptive PID control, pole configuration adaptive PID cont...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 刘艳斌王轻何龙祥葛继成
Owner 河南驼人医疗器械研究院有限公司
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