Intracranial pressure measuring device of BP neural network model based on genetic algorithm optimization and working method thereof
A BP neural network and genetic algorithm technology, applied in the field of intracranial pressure measurement devices, to achieve the effect of removing the noise of the instrument itself, maintaining true reliability, and low power consumption
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Embodiment 1
[0064] A kind of intracranial pressure measurement device based on the BP neural network model optimized by genetic algorithm, such as figure 1 and Figure 5 As shown, it includes LED light source 1, fiber optic coupler 2, fiber optic pressure sensor, spectrometer 5, system microprocessor, LCD display and power supply,
[0065] The output end of the LED light source 1 is connected to the first input end of the optical fiber coupler 2, the output end of the optical fiber coupler 2 is respectively connected to one end of the optical fiber sensor 3 and the optical switch 4, and the other end of the optical switch 4 is sequentially connected to the spectrometer 5. The system microprocessor is connected with the LCD display;
[0066] The optical signal sent by LED light source 1 is input into the optical fiber pressure sensor through the optical fiber coupler 2, and the reflected optical signal sent by the optical fiber pressure sensor is input into the optical fiber coupler 2, an...
Embodiment 2
[0072] The working method of the intracranial pressure measuring device based on the BP neural network model optimized by genetic algorithm that embodiment 1 provides, such as Figure 2-Figure 4 shown, including:
[0073] (1) collect spectral data by spectrometer 5, carry out normalization process to the collected spectral data, construct spectral data set, and spectral data set is divided into training set and test set;
[0074] In step (1), the process of normalization processing is: In the formula, I i is the i-th original light intensity value, I max is the maximum light intensity value, I min is the minimum light intensity value, is the i-th light intensity value after normalization. The value of i is 1-100, and 100 sets of spectral data are sampled to obtain a corresponding pressure value.
[0075] (2) Determine the BP neural network model, then initialize the BP neural network model to obtain the initial values of weights and thresholds;
[0076] In step (2),...
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