Method for soft measurement of nuclear power station reactor core temperature fields on basis of neutral network surface fitting
A neural network and surface fitting technology, applied in the field of thermal measurement, can solve problems such as difficulty in obtaining the temperature distribution inside the furnace or reactor, difficulty in measuring temperature, and inability to guarantee the real-time performance of the algorithm, so as to avoid local minimum problems, The effect of strong memory ability and accelerated learning speed
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[0036] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.
[0037] The main process of this patent implementation scheme is specifically as follows (see figure 1 ):
[0038] (1) The method of radial basis function neural network surface fitting that adopts is carried out the concrete realization that n measuring point temperature is calculated and processed as follows (see Image 6 ):
[0039] The output of the kth node in the hidden layer of the RBF neural network can be expressed as r k =R(‖X i -T k ‖); the output of the jth node in the output layer can be expressed as RBF radial basis function R(·) is a Gaussian function (wherein, c is the center of the Gaussian function, and σ is the variance of the Gaussian function), then the output of the RBF neural network, that is, the fitted outlet temperature is:
[0040]
[0041] where x p is the sensor temperature measuring point data as the p...
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