US 2023/0274148 A1 · ATMOSPHERIC VISIBILITY PREDICTION METHOD BASED ON DBN

Abstract

An atmospheric visibility prediction method based on deep belief networks (DBN) includes steps of establishing a DBN model, determining a network input parameter, preprocessing input data, preferably selecting the number of hidden layers and the number of nodes in each layer, training the DBN model and predicting atmospheric visibility. According to the method, an output layer of the DBN model is a back propagation (BP) network, an output feature vector of a restricted Boltzmann machine (RBM) is received as an input feature vector, and an entity relationship classifier is trained in a supervised way. A process of training a model by an RBM network can be regarded as initialization of a deep BP network weight parameter, such that the DBN overcomes defects that the BP network is prone to local optimization and is long in training time.

An atmospheric visibility prediction method based on deep belief networks (DBN), comprising:

step 1:
establishing a DBN model comprising an input layer, hidden layer l-hidden layer n and an output layer, wherein the input layer, the hidden layer l-hidden layer n and the output layer are cascaded in sequence, wherein the output layer is a back propagation network;
the hidden layer l-hidden layer n are restricted Boltzmann machines;
a corresponding output terminal of the input layer is connected to a corresponding input terminal of the hidden layer 1;
and a corresponding output terminal of a hidden layer i is connected to a corresponding input terminal of a hidden layer i+1, l<i<n, and a corresponding output terminal of the hidden layer n is connected to a corresponding input terminal of the output layer;
step 2:
determining a network input parameter:
using a principal component analysis method to determine a type of the network input parameter;
step 3:
preprocessing input data:
normalizing the input data in advance, and dividing the input data into a training set and a prediction set;
step 4:
preferably selecting a number of hidden layers and a number of nodes in each layer:
preferably selecting the number of hidden layers with a visibility prediction accuracy as a target within a predetermined range of a number of layers according to a step size of a preset number of layers, and then preferably selecting the number of nodes in the hidden layers with the visibility prediction accuracy as a target within a predetermined range of the number of nodes according to a preset number of nodes in the hidden layers;
step 5:
training the DBN model:
pre-training initial parameters of the hidden layer l-hidden layer n layer by layer, and then adjusting the initial parameters of each hidden layer finely through an error back propagation method,
wherein each hidden layer comprises 1 visible layer and 1 hidden layer, and the initial parameters of each hidden layer comprise a weighting matrix W, a visible layer bias coefficient vector a and a hidden layer bias coefficient vector b;
an energy function is:
E(v,h)=−aTv−bTh−hTWv (1)
an optimization objective function is:
L(W,a,b)=−Σ ln(P(V(i))) (2)
a probability that the nodes in the hidden layers convert from a visual state to a visible state is:
P(hj=1|v)=sigmoid(bj+Wj,:v) (3)
a probability that the nodes in the hidden layers convert from the visual state to a hidden state is:
P(vj=1|h)=sigmoid(aj+W:,jTh) (4)
an objective function of adjusting the initial parameters of each hidden layer finely through the error back propagation method is:
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in the formula, a first term is an error term, and a second term is called a regularization term, wherein the first term and the second term are used to control an element size of a weight matrix of each layer, to prevent the weight matrix from being too large and avoid over-fitting of a network model;
a variable δi(l) is a partial derivative of a final error for a variable of a node in each layer before an activation function, wherein the variable δi(l) is used to measure a contribution value of a certain node in a certain layer for the final error, and an expression of δi(l) is as follows:
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for a final layer, that is, layer L,
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wherein
f′(zi(L))=ai(L)(1−ai(L)) (8)
for other layers (l=L−1, L−2, . . . 2),
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wherein
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the weighting matrix W, the visible layer bias coefficient vector a, and the hidden layer bias coefficient vector b are iterated until a difference between two iteration results is less than a preset threshold, and a parameter update method is:
update formulas of various parameters are as follows:
W
i
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=
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-
α
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(
12
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b
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wherein a is a learning rate;
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a
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(
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∂
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∂
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(
15
)
for the output layer:
δ(L)=−(y−a(L))·f′(z(L)) (16)
for other layers (l=L−1, L−2, . . . 2):
δ(l)=[(W(l))Tδ(l+1)]·f′(z(l)) (17)
a partial derivative of an objective function of each sample for each parameter is used as a feedback control signal, and weight update is controlled to minimize a loss function;
and
step 6:
predicting atmospheric visibility:
using prediction, data and a trained DBN model to predict the atmospheric visibility.

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Atmospheric visibility prediction method based on dbn

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Atmospheric visibility prediction method based on dbn

Life of this patent

Abstract

Claims3