ST-GRU memristor neural network circuit for realizing gait prediction and training method

A ST-GRU, neural network technology, applied in the field of neural network circuits, can solve problems such as inability to target training, and achieve the effect of strengthening the ability to deal with dynamic prediction problems, high accuracy, and good computing efficiency

Pending Publication Date: 2022-04-12
ANHUI UNIVERSITY
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The present invention improves the deficiencies in the timing prediction of the existing GRU hardware circuit, solves the problem that the existing GRU hardware circuit cannot train the target in the multi-layer dynamic timing prediction, realizes the sequence prediction of multiple dimensions and It is used for the step size prediction problem of time series, has high accuracy and good calculation efficiency, and does not need to consume a lot of software computing power resources

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • ST-GRU memristor neural network circuit for realizing gait prediction and training method
  • ST-GRU memristor neural network circuit for realizing gait prediction and training method
  • ST-GRU memristor neural network circuit for realizing gait prediction and training method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0034] (1) The GRU unit circuit for processing time series is mainly composed of a memristor array; the GRU unit circuit for processing space series is mainly composed of a memristor array; for example: the time series for processing The GRU unit circuit can be employed as Figure 4 The circuit shown ( Figure 4 The memristor array is represented in the three boxes of ), the GRU unit circuit for processing spatial sequences can be adopted as Figure 4 The circuit shown ( Figure 4 The memristor array is represented in three boxes). Because the memristor has a non-volatile memory function, its volume and power consumption are smaller than traditional memory, and it does not need to refresh regularly like DRAM, so the present invention preferably selects the memristor as the unit of the neural network structure.

[0035] (2) The GRU unit circuit for processing the time series processes the time characteristic pulse voltage signal at the current time in the time series as the ...

Embodiment 1

[0047] Such as figure 2 , image 3 and Figure 4 As shown, a ST-GRU memristive neural network circuit for gait prediction, its structure includes: GRU unit circuit for processing time series, GRU unit circuit and fully connected layer circuit for processing space series; time series Each moment in each corresponds to a space sequence; the GRU unit circuit for processing the time sequence processes the time characteristic pulse voltage signal at each moment in the time sequence, and the GRU unit circuit for processing the space sequence The characteristic pulse voltage signal of the spatial sequence corresponding to each moment is processed.

[0048] For the time characteristic pulse voltage signal at each moment in the time series, after the GRU unit circuit for processing the time series processes the time characteristic pulse voltage signal at a moment in the time series, the The output result of the GRU unit circuit is used as the spatial characteristic pulse voltage si...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an ST-GRU memristor neural network circuit for realizing gait prediction and a training method, and the circuit comprises a GRU unit circuit which is used for processing a time sequence and processing a time characteristic pulse voltage signal at a moment in the time sequence; acquiring an output result of the GRU unit circuit for processing the time sequence as a spatial characteristic pulse voltage signal, and inputting the spatial characteristic pulse voltage signal into the GRU unit circuit for processing the spatial sequence for processing the characteristic pulse voltage signal of the spatial sequence corresponding to the moment; an output result of the GRU unit circuit for processing the spatial sequence is input to the fully connected layer circuit. The problem that an existing GRU hardware circuit cannot train a target in multi-layer dynamic time sequence prediction is solved, multi-dimensional sequence prediction is achieved, the multi-dimensional sequence prediction is used for time sequence step length prediction, high accuracy and good calculation efficiency are achieved, and a large number of software computing power resources do not need to be consumed.

Description

technical field [0001] The invention relates to the field of neural network circuits, in particular to an ST-GRU memristive neural network circuit and a training method for realizing gait prediction. Background technique [0002] Much of the attention paid to artificial intelligence in recent years is due to advances in deep neural networks. There are many structures of deep neural networks, and recurrent neural network (RNN) is one of the important structures. There are many improved versions of the cyclic neural network, the more commonly used ones are long short-term memory (LSTM) and gate controllable recurrent unit (GRU), etc. These versions can effectively improve the gradient disappearance and gradient explosion problems in RNN; but due to the single The recurrent neural network has hysteresis, so it is not very effective when dealing with forecasting problems. [0003] Memristor is a nanoscale device with high volatility, high density, low power consumption, and co...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/063G06N3/08
Inventor 吴祖恒冯哲王旭代月花
Owner ANHUI UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products