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Temperature compensation method and system for silicon micro-accelerometer based on improved PSO (Particle Swarm Optimization) optimized neural network

A BP neural network and accelerometer technology, applied in neural learning methods, biological neural network models, speed/acceleration/shock measurement, etc. The effect of assembling, maintaining variety, increasing possibility

Inactive Publication Date: 2018-06-05
SUZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the existing technology compensates the temperature influence of the silicon micro accelerometer from the aspects of sensitive structure and process, working environment and software compensation in the circuit system.
Although the sensitive structure and process improvement can have a certain effect on temperature drift, it cannot meet the engineering requirements, and the software compensation in the existing achievements has great limitations on the full temperature performance and calculation complexity.
In particular, there is no good report on the compensation performance in the full temperature range. Even the PSO_BP compensation method has not been applied in MEMS accelerometers.

Method used

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  • Temperature compensation method and system for silicon micro-accelerometer based on improved PSO (Particle Swarm Optimization) optimized neural network
  • Temperature compensation method and system for silicon micro-accelerometer based on improved PSO (Particle Swarm Optimization) optimized neural network
  • Temperature compensation method and system for silicon micro-accelerometer based on improved PSO (Particle Swarm Optimization) optimized neural network

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Embodiment 1

[0060] see Figures 1 to 3 As shown, the present embodiment is based on the silicon micro-accelerometer temperature compensation method of the improved PSO optimized neural network, including:

[0061] S1 measures multiple groups of acceleration outputs and real-time temperature values ​​of the accelerometer at N temperature points, and calculates the average value of the acceleration outputs and real-time temperature values ​​as a training sample for PSO optimization and BP neural network;

[0062] S2 Build a BP neural network based on training samples, and set the number of neurons in the input, output and hidden layers of the BP neural network, the transfer function of each layer, and network training parameters, and use the optimal extreme point optimized by PSO as the BP neural network. The initial weights and thresholds of the network model are used to train the BP neural network to judge whether it meets the requirements of the BP neural network training, and if so, jum...

Embodiment 2

[0097] This embodiment is based on the silicon micro-accelerometer temperature compensation system of the improved PSO optimized neural network, including: a temperature control box, a microprocessor;

[0098] The temperature control box is set with N successively increasing temperatures, and measures multiple sets of acceleration output and real-time temperature values ​​of silicon micro-accelerometers at N temperature points respectively, as a result of PSO extreme point optimization and BP neural network. Training samples, outputting the training samples to the microprocessor;

[0099] The microprocessor includes: a PSO optimization unit, a BP neural network unit, a compensation unit,

[0100] The PSO optimization unit operates a particle swarm optimization algorithm to optimize the optimal extreme point;

[0101] The BP neural network unit constructs a BP neural network, sets the number of neurons of the BP neural network input, output layer and hidden layer, each layer tra...

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Abstract

The invention relates to a temperature compensation method and system for a silicon micro-accelerometer based on an improved PSO (Particle Swarm Optimization) optimized neural network, and the temperature compensation method and the temperature compensation system are designed for improving temperature compensation precision. The method comprises the following steps: acquiring a training sample ofPSO optimization and a BP (Back Propagation) neutral network; constructing the BP neutral network on the basis of the training sample; using an optimal extreme point optimized by an adaptive weight PSO as an initial weight value and a threshold value of a BP neutral network model; introducing mutation operation into a PSO algorithm, updating the particles, then reinitializing the particles at a certain probability and expanding a population search space which is continuously reduced in iteration by the mutation operation; establishing the BP neutral network by calling the parameters, realizing real-time temperature compensation of the silicon micro-accelerometer and outputting a compensation result. The temperature compensation method and the temperature compensation system disclosed by the invention have the advantages that the problems of solving of an optimal compensation result and temperature globality are solved, and finally improved compensation precision and global improvementof the silicon micro-accelerometer are realized.

Description

technical field [0001] The invention belongs to the field of temperature compensation of MEMS accelerometers, and in particular relates to a method and system for temperature compensation of silicon micro-accelerometers based on an improved PSO optimized neural network. Background technique [0002] The MEMS accelerometer is one of the very important components in the micro-miniature inertial navigation system, and its performance has a direct impact on the attitude, speed and positioning accuracy of the navigation system. It has the advantages of small size, light structure and low cost, so it is widely used in many fields such as military, industry and commerce. Environmental factors are an important factor affecting the accuracy of the system, and temperature is an important factor affecting the reliability and accuracy of MEMS accelerometers. As the ambient temperature changes, due to the noise of silicon-based materials, the effects of thermal expansion and contraction...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C25/00G01P21/00G06N3/08
Inventor 徐大诚王法亮
Owner SUZHOU UNIV
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