Water level prediction method of voltage regulator based on cost-sensitive lstm recurrent neural network
一种循环神经网络、代价敏感的技术，应用在船舶核动力稳压器水位预测领域，能够解决模型预测能力影响大等问题，达到强学习能力和预测能力、好鲁棒性和稳定性、收敛快的效果
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Embodiment 1
[0040] A method for predicting the water level of a voltage regulator based on a cost-sensitive LSTM recurrent neural network, comprising the following steps:
[0041] S1, select p parameters x with a high degree of coupling with the water level of the regulator t ∈R p as an input parameter.
[0042] S2. Construct the LSTM-based regulator water level prediction model and its framework. The specific step S2 includes two steps, S21, applying the RNN model to calculate the hidden layer sequence and the output sequence. The difference between the structure of RNN neuron and standard neuron is that it has a loop structure, which can transfer the information of the previous state to the current state, such as figure 1 As shown, when the input is a time series, it can be unrolled into a series of interconnected standard neurons. For an input sequence X=(x 1 ; x 2 ;…;x n ), applying a standard RNN model (such as figure 2 shown), a hidden layer sequence H=(h 1 , h 2 ,...,h ...
Embodiment 2
[0059] The difference between this embodiment and Embodiment 1 is that in this embodiment, the LSTM model is modified, and the traditional LSTM regression model uses formula (9) as the loss function, which actually implies an assumption: the prediction error of the training sample have the same weight. In the experiment, we found that this is unreasonable. Therefore, the sample weight variable was introduced to improve the loss function of the original model. The improved model loss function is as follows:
[0060]
[0061] Among them, r t is the sample weight at time t. The error of the LSTM model will increase significantly in the following two cases:
[0062] 1) when y t second derivative with respect to time When increasing, the usual error will also increase suddenly. image 3 middle curve y t at point p t The slope changes significantly, and the error The rate of increase over time also points p t increased significantly afterwards. Introduce parameter a ...
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