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Adaptive enhancer module for radial basis function neural network based on FPGA design

An adaptive enhancement and neural network technology, applied in the field of radial basis function neural network adaptive enhancer module, can solve the problems of lack of dynamic variation information, time-consuming, inability to meet real-time monitoring, etc., to achieve fast computing functions and improve performance , stable and reliable performance

Active Publication Date: 2018-03-13
INST OF BIOMEDICAL ENG CHINESE ACAD OF MEDICAL SCI
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Problems solved by technology

[0002] At present, the existing medical monitoring equipment on the market is based on the average superposition technology to pick up evoked potentials. The main disadvantages are: time-consuming and lack of dynamic variation information
With the continuous development of signal processing technology, various new methods and ideas have been applied to the rapid extraction of evoked potentials, but most of the algorithms are currently limited to the offline working mode of the laboratory, and are implemented on PCs, which cannot meet the requirements of real-time monitoring. Requirements, only when the real-time and fast calculation of the algorithm is realized, can the productization be truly realized

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  • Adaptive enhancer module for radial basis function neural network based on FPGA design
  • Adaptive enhancer module for radial basis function neural network based on FPGA design
  • Adaptive enhancer module for radial basis function neural network based on FPGA design

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Embodiment Construction

[0033] Embodiments of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0034]A radial basis function neural network adaptive enhancer module designed based on FPGA is realized on FPGA. In this embodiment, the FPGA adopts the Vertex4 chip of Xilinx Company. In the following description, all comparators, all registers, all registers, all dividers, all integer multipliers, all memories, all decimal multipliers, all decimal adders, and inverters The phase devices are all IP cores designed by Xilinx for their own company's FPGA, among which the decimal multiplier and integer multiplier are modified on the basis of the IP core of Xilinx's 22-bit integer multiplier, and the decimal multiplier is obtained from Xilinx The upper 22 bits of the 44-bit output of the company's 22-bit integer multiplier are used as the output of the decimal multiplier, and the integer multiplier takes the lower 22 bits of the 44-bit output of X...

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Abstract

The invention relates to an adaptive enhancer module for a radial basis function neural network based on FPGA design. The adaptive enhancer module is characterized by being formed by connecting a signal conversion circuit, a radial basis function circuit, a signal adjusting circuit, an LMS filter circuit and an output adjusting circuit, wherein the input end of the signal conversion circuit is connected to an original signal, and the output end of the signal conversion circuit is connected to the input end of the signal adjusting circuit and the input end of the radial basis function circuit;the output end of the radial basis function circuit is connected to another input end of the signal adjusting circuit; the two output signals of the signal adjusting circuit are respectively connectedto the LMS filter circuit; the input end of the output adjusting circuit is respectively connected to the output end of the LMS filter circuit, an adjusting signal m and an adjusting signal n, and asignal for removing noise is output. According to the adaptive enhancer module, the design is reasonable, the performance of the conventional LMS filter is improved, a fast calculation function is realized, the stability and the reliability of the performance are guaranteed, and the requirements for real-time monitoring of the somatosensory evoked potential can be met.

Description

technical field [0001] The invention relates to the technical field of digital filtering, in particular to an FPGA-based radial basis function neural network adaptive enhancer module. Background technique [0002] At present, the existing medical monitoring equipment on the market is based on the average superposition technology to pick up evoked potentials. The main disadvantages are: time-consuming and lack of dynamic variation information. Delay in the detection of evoked potentials may delay the diagnosis of spinal cord injury, and may miss the opportunity for the operator to perform remedial actions, resulting in irreversible neurological damage. With the continuous development of signal processing technology, various new methods and ideas have been applied to the rapid extraction of evoked potentials, but most of the algorithms are currently limited to the offline working mode of the laboratory, and are implemented on PCs, which cannot meet the requirements of real-tim...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H21/00
CPCH03H21/0043
Inventor 胡勇崔红岩谢小波冯莉高松坤柯丽萍
Owner INST OF BIOMEDICAL ENG CHINESE ACAD OF MEDICAL SCI
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