Sea clutter optimal soft measuring instrument and method based on wavelet neural network optimized by fruit fly optimization algorithm
A technology of wavelet neural network and fruit fly optimization algorithm, applied in biological neural network models, design optimization/simulation, instruments, etc., can solve the problems of poor generalization performance, low sensitivity to noise, and low measurement accuracy
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Embodiment 1
[0078] refer to figure 1 , figure 2 and image 3 , an optimal soft measurement instrument for sea clutter based on fruit fly optimization algorithm to optimize wavelet neural network, including radar 1, on-site intelligent instrument for measuring easily measurable variables 2, control station for measuring operating variables 3, storing data The on-site database 4 and the sea clutter soft measurement value display instrument 6, the on-site intelligent instrument 2, the control station 3 are connected to the radar 1, the on-site intelligent instrument 2, the control station 3 are connected to the on-site database 4, the soft sensor The instrument also includes the optimal soft sensor host computer 5 optimized by the fruit fly optimization algorithm to optimize the wavelet neural network, and the on-site database 4 is connected to the input end of the optimal soft sensor host computer 5 based on the fruit fly optimization algorithm optimized wavelet neural network, The outpu...
Embodiment 2
[0112] refer to figure 1 , figure 2 and image 3 , a sea clutter optimal soft sensor method based on the fruit fly optimization algorithm to optimize the wavelet neural network, the soft sensor method comprises the following steps:
[0113] 1) For the radar object, according to the process analysis and operation analysis, select the operational variables and easily measurable variables as the input of the model, and the operational variables and easily measurable variables are obtained from the on-site database;
[0114] 2) Preprocess the model training samples input from the on-site database, and centralize the training samples, that is, subtract the average value of the samples, and then standardize them so that the mean value is 0 and the variance is 1. This processing is accomplished using the following algorithmic procedure:
[0115] 2.1) Calculate the mean:
[0116] 2.2) Calculate the variance:
[0117] 2.3) Standardization:
[0118] Among them, TX is the tr...
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