CN112834034A - Neuron network correction wavelength UV generator and method based on single chip microcomputer - Google Patents
Neuron network correction wavelength UV generator and method based on single chip microcomputer Download PDFInfo
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Abstract
The invention discloses a neuron network correction wavelength UV generator and a neuron network correction wavelength UV method based on a single chip microcomputer. The invention adopts a neural network (BP) control strategy of error feedback transmission control, and constructs a practical controller by utilizing the principle that the BP neural network resists the influence of environmental factors and is infinitely iterated to approach. And giving an accurate control quantity so as to control the wavelength (lambda) of Ultraviolet (UV) light and achieve the aim of stabilizing the output wavelength at a given wavelength.
Description
Technical Field
The invention belongs to the technical field of UV generator wavelength correction, and particularly relates to a neuron network wavelength correction UV generator and a method based on a single chip microcomputer.
Background
There is an important principle or purpose for using a spectrophotometer for ultraviolet and visible light or manufacturing an ultraviolet and visible spectrophotometer, which is to make the instrument stable and reliable.
If the stability of one instrument is poor, it is impossible to obtain good analytical test results. The stability of one instrument included Baseline Drift (Baseline Drift) and Photometric repeatability (Photometric repeatability).
The repeatability of baseline drift and luminosity has a close relationship with the use environment of the online detection instrument, wherein the main factors are the digestion tank temperature (T) and the concentration d of the detection solutionADetecting the concentration d of the solutionADetecting the concentration d of the solutionADetecting the dosage q of the solutionADetecting the dosage q of the solutionBMeasuring the dosage q of the liquidCHumidity (h), and the effects of these factors are characterized by non-linearity.
Disclosure of Invention
The invention aims to solve the technical problem of providing a neuron network wavelength correction UV generator and a method based on a single chip microcomputer for overcoming the defects of the background art. Gives an accurate control amount to control the Ultraviolet (UV) visible spectral wavelength (λ).
The invention adopts the following technical scheme for solving the technical problems:
a neuron network correction wavelength UV generator based on a single chip microcomputer comprises a UV generator, a signal difference processing unit, a BP neuron network controller and a power amplifier which are sequentially connected, wherein the power amplifier is connected with the input end of the signal difference processing unit through a transmitter.
Further, the BP neuron network controller adopts an AT89C51 single chip microcomputer controller.
Furthermore, the BP neural network controller adopts a four-layer structure form, namely an input layer, a middle layer and an output layer; nine neurons are used in the input layer, four neurons are used in the middle layer, and one neuron is used in the output; the 9 inputs form an input vector: x ═ X1,x2,…,xi,…,xn)T；
Then go toAnd (3) passing a weight matrix from the input layer to the middle layer: v ═ V (V)1,V2,…,Vj,…,Vm) Obtaining: output vector of intermediate layer: y ═ Y1,y2,…,yj,…,ym)T；
Then, through a weight matrix from the middle layer to the output layer: w ═ W1,W2,…,Wk,…,Wl) And obtaining an output layer output vector: o ═ O (y).
A method for correcting wavelength of a UV generator by utilizing a neuron network based on a single chip microcomputer comprises the following steps:
s1, inputting a given UV wavelength into the comparator, wherein in an initial state, the other end of the signal difference processing unit has no input, so that the signal difference processing unit converts the initial UV wavelength signal into a digital signal and inputs the digital signal into the BP neural network controller;
s2, the BP neural network controller receives the digital signal and simultaneously receives 8 influencing factor data including digestion pool temperature T, detection solution concentration dA, detection solution concentration dB, detection solution concentration dC, detection solution dosage q _ A, detection solution dosage qB, detection solution dosage qC and humidity h;
s3, the BP neural network controller processes the received digital signals and the influencing factor data to obtain control signals and converts the control signals into specific UV wavelength signals;
s4, the power amplifier amplifies the specific UV wavelength signal and transmits the amplified signal to the transmitter;
s5, the transmitter transmits the specific UV wavelength signal to the signal difference processing unit, and the specific UV wavelength signal is compared with the given UV wavelength signal in the signal difference processing unit to make difference;
s6, the signal difference value processing unit converts the comparison and difference result into a digital signal and transmits the digital signal to the BP neural network controller again;
s7, repeating the steps S2-S6, and continuously correcting the given UV wavelength.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the invention adopts a neural network (BP) control strategy of error feedback transmission control, and constructs a practical controller by utilizing the principle that the BP neural network has infinite iterative approximation on factor influence. Gives an accurate control amount to control the Ultraviolet (UV) visible spectral wavelength (λ).
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a block diagram of a BP neural network according to the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
a single chip microcomputer-based UV generator for correcting a wavelength of a neural network comprises a UV generator, a signal difference processing unit, a BP neural network controller and a power amplifier which are sequentially connected, wherein the power amplifier is connected with the input end of the signal difference processing unit through a transmitter. The BP neuron network controller adopts an AT89C51 single chip microcomputer controller. The BP neural network controller adopts a four-layer structure form, namely an input layer, a middle layer and an output layer; nine neurons are used in the input layer, four neurons are used in the middle layer, and one neuron is used in the output; the 9 inputs form an input vector: x ═ X1,x2,…,xi,…,xn)T；
Then, through a weight matrix from the input layer to the middle layer: v ═ V (V)1,V2,…,Vj,…,Vm) Obtaining: output vector of intermediate layer: y ═ Y1,y2,…,yj,…,ym)T；
Then, through a weight matrix from the middle layer to the output layer: w ═ W1,W2,…,Wk,…,Wl) And obtaining an output layer output vector: o ═ O (y).
A method for correcting wavelength of a UV generator for correcting wavelength by using a neuron network based on a single chip microcomputer comprises the following steps:
s1, inputting a given UV wavelength into the signal difference processing unit, wherein in an initial state, the other end of the signal difference processing unit has no input, so that the signal difference processing unit converts the initial UV wavelength signal into a digital signal and inputs the digital signal into the BP neural network controller;
s2, the BP neural network controller receives the digital signal and simultaneously receives 8 influencing factor data including digestion pool temperature T, detection solution concentration dA, detection solution concentration dB, detection solution concentration dC, detection solution dosage q _ A, detection solution dosage qB, detection solution dosage qC and humidity h;
s3, the BP neural network controller processes the received digital signals and the influencing factor data to obtain control signals and converts the control signals into specific UV wavelength signals;
s4, the power amplifier amplifies the specific UV wavelength signal and transmits the amplified signal to the transmitter;
s5, the transmitter transmits the specific UV wavelength signal to the signal difference processing unit, and the specific UV wavelength signal is compared with the given UV wavelength signal in the signal difference processing unit to make difference;
s6, the signal difference value processing unit converts the comparison and difference result into a digital signal and transmits the digital signal to the BP neural network controller again;
s7, repeating the steps S2-S6, and continuously correcting the given UV wavelength.
Description of specific embodiments:
there is an important principle or purpose for using a spectrophotometer for ultraviolet and visible light or manufacturing an ultraviolet and visible spectrophotometer, which is to make the instrument stable and reliable. If the stability of one instrument is poor, it is impossible to obtain good analytical test results. The stability of one instrument included Baseline Drift (Baseline Drift) and Photometric repeatability (Photometric repeatability). The repeatability of baseline drift and luminosity has a close relationship with the use environment of the online detection instrument, wherein the main factors are the digestion tank temperature (T) and the concentration d of the detection solutionADetecting the concentration d of the solutionBDetecting the concentration d of the solutionCDetecting the dosage q of the solutionADetecting the dosage q of the solutionBMeasuring the dosage q of the liquidCHumidity (h), and the effects of these factors are characterized by non-linearity.
As shown in fig. 1, the present invention adopts a neural network (BP) control strategy of error feedback transmission control, and constructs a practical controller by using the principle that the BP neural network has infinite iterative approximation on the influence of factors. The system is realized based on a general 51-system single chip microcomputer and an AT89C51 minimum system.
The BP neural network has a structure that a BP neural network controller adopts a four-layer structure form, namely an input layer, a middle layer (hidden layer) and an output layer. Nine neurons are used for the input layer, four neurons are used for the middle layer, and one neuron is used for the output. W(i)(i＝1.2.3…L)And the weight coefficient selected by nonlinear iteration is obtained by learning. The 9 inputs form an input vector. Wherein,
inputting a vector: x ═ X1,x2,…,xi,…,xn)T；
Output vector of intermediate layer: y ═ Y1,y2,…,yj,…,ym)T；
Output layer output vector: o ═ O (y);
inputting a weight matrix from the layer to the hidden layer: v ═ V (V)1,V2,…,Vj,…,Vm)；
Weight matrix from hidden layer to output layer: w ═ W1,W2,…,Wk,…,Wl)。
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention. While the embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (4)
1. A neuron network correction wavelength UV generator based on single chip microcomputer implementation is characterized in that: the device comprises a UV generator, a signal difference value processing unit, an error Back Propagation (BP) neural network controller and a power amplifier which are connected in sequence, wherein the power amplifier is connected with the input end of the signal difference value processing unit through a transmitter.
2. The neuron network correction wavelength UV generator realized based on the single chip microcomputer according to claim 1, characterized in that: and the error Back Propagation (BP) neuron network controller adopts a general AT89C51 singlechip.
3. The neuron network correction wavelength UV generator realized based on the single chip microcomputer according to claim 1, characterized in that: the BP neural network controller adopts a four-layer structure form, namely an input layer, a middle layer and an output layer; nine neurons are used in the input layer, four neurons are used in the middle layer, and one neuron is used in the output; the 9 inputs form an input vector: x ═ X1,x2,…,xi,…,xn)T；
Then, through a weight matrix from the input layer to the middle layer: v ═ V (V)1,V2,…,Vj,…,Vm) Obtaining: output vector of intermediate layer: y ═ Y1,y2,…,yj,…,ym)T；
Then, through a weight matrix from the middle layer to the output layer: w ═ W1,W2,…,Wk,…,Wl) And obtaining an output layer output vector: o ═ O (y).
4. The method for correcting the wavelength of the UV generator by utilizing the neural network correction wavelength realized based on the single chip microcomputer according to claim 1 is characterized in that:
the method comprises the following steps:
s1, inputting a given UV wavelength into the comparator, wherein in an initial state, the other end of the signal difference processing unit has no input, so that the signal difference processing unit converts the initial UV wavelength signal into a digital signal and inputs the digital signal into the BP neural network controller;
s2, the BP neuron network controller receives the digital signal and simultaneously receives the digital signal including the digestion pool temperature (T) and the detection solution concentration dADetecting the concentration d of the solutionADetecting the concentration d of the solutionADetecting the dosage q of the solutionADetecting the dosage q of the solutionBMeasuring the dosage q of the liquidC8 influence factor data including humidity (h);
s3, the BP neural network controller processes the received digital signals and the influencing factor data to obtain control signals and converts the control signals into specific UV wavelength signals;
s4, the power amplifier amplifies the specific UV wavelength signal and transmits the amplified signal to the transmitter;
s5, the transmitter transmits the specific UV wavelength signal to the signal difference processing unit, and the specific UV wavelength signal is compared with the given UV wavelength signal in the signal difference processing unit to make difference;
s6, the signal difference value processing unit converts the comparison and difference result into a digital signal and transmits the digital signal to the BP neural network controller again;
s7, repeating the steps S2-S6, and continuously correcting the given UV wavelength.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4904088A (en) * | 1984-08-10 | 1990-02-27 | U.S. Philips Corporation | Method and apparatus for determining radiation wavelengths and wavelength-corrected radiation power of monochromatic light sources |
| CN101501465A (en) * | 2006-07-18 | 2009-08-05 | Tir科技公司 | Method and apparatus for determining intensities and peak wavelengths of light |
| CN109791958A (en) * | 2016-09-27 | 2019-05-21 | 日机装株式会社 | UV curing apparatus |
| CN111542152A (en) * | 2020-05-22 | 2020-08-14 | 中国科学院半导体研究所 | Ultraviolet light source system with automatic feedback correction function of light intensity and its application |
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- 2020-12-31 CN CN202011629846.3A patent/CN112834034A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4904088A (en) * | 1984-08-10 | 1990-02-27 | U.S. Philips Corporation | Method and apparatus for determining radiation wavelengths and wavelength-corrected radiation power of monochromatic light sources |
| CN101501465A (en) * | 2006-07-18 | 2009-08-05 | Tir科技公司 | Method and apparatus for determining intensities and peak wavelengths of light |
| CN109791958A (en) * | 2016-09-27 | 2019-05-21 | 日机装株式会社 | UV curing apparatus |
| CN111542152A (en) * | 2020-05-22 | 2020-08-14 | 中国科学院半导体研究所 | Ultraviolet light source system with automatic feedback correction function of light intensity and its application |
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