Weather prediction method
Weather prediction method
 CN 102,622,515 A
 Filed: 02/21/2012
 Published: 08/01/2012
 Est. Priority Date: 02/21/2012
 Status: Active Application
First Claim
1. weather forecasting method based on the BP neural network may further comprise the steps:
 Step 1, the original training data matrix of reception and training duration parameters;
Step 2, initialization data comprise each neuron output initial value of maximum times, inertial coefficient, hidden layer and output layer of setting learning rate, anticipation error, training, dynamically obtain row matrix column data p0 according to raw data;
Step 3, the maximal value maxv (j) that obtains every row training data carry out normalization with minimum value minv (j) back to data and handle, and make raw data standard to 0 between 1;
Step 4, obtain the input matrix and the output matrix of training sample according to original training data;
Step 5, random initializtion weight matrix wki and wij, wki represent to hide the weight matrix of layer to input layer, and wij representes that input layer arrives the weight matrix of hiding layer;
Each neuron of layer, each neuronic output of output layer are hidden in step 6, calculating;
Step 7, calculate the error that each output and hidden neuron calculate output, the weights in the network are upgraded in backpropagation;
Step 8, repeating step 6, till satisfying end condition, the end condition of this algorithm is an error less than anticipation error or frequency of training greater than maximum set value.
Chinese PRB Reexamination
Abstract
The invention discloses a weather prediction method, which includes the steps: providing temperature information values, normalizing the temperature information values, establishing an input/output matrix of a training sample, predicting weather through a neural network based on the output matrix, and the like. Original training data modes can be automatically distinguished by an improved algorithm, and are subjected to sample establishing and normalization. The weather prediction method is applicable to various complex circumstances, is high in flexibility, and needs no auxiliary data to complete prediction, and prediction results can be restored within a numeric range corresponding to original training data.

16 Citations
A kind of weather prediction method based on 1DCNN and BiLSTM  
Patent #
CN 110,059,082 A
Filed 04/17/2019

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A kind of weather characteristics register on user the measure of influence  
Patent #
CN 108,364,098 A
Filed 02/08/2018

Current Assignee

Fixedpoint environment state forecast method and apparatus  
Patent #
CN 108,594,334 A
Filed 10/13/2014

Current Assignee

Windscreen wiper with automatic starting device and method for  
Patent #
CN 109,034,265 A
Filed 08/16/2018

Current Assignee

Climatic change scenario revision method capable of reducing uncertainty  
Patent #
CN 104,298,877 A
Filed 10/13/2014

Current Assignee

Design of image pattern noise relevance predictor  
Patent #
CN 105,376,506 A
Filed 08/27/2014

Current Assignee

Hail Forecasting Methodology based on kurtosis Yu angle second moment  
Patent #
CN 106,097,399 A
Filed 06/08/2016

Current Assignee

The Design of Mathematical Model method of hail prediction  
Patent #
CN 106,097,400 A
Filed 06/08/2016

Current Assignee

Hail shooting index analysis method based on critical point design  
Patent #
CN 106,097,401 A
Filed 06/08/2016

Current Assignee

Method based on the hail cloud recognition estimating excavation  
Patent #
CN 106,096,545 A
Filed 06/08/2016

Current Assignee

The hail cloud Forecasting Methodology of 6 rank data characteristicses  
Patent #
CN 106,096,546 A
Filed 06/08/2016

Current Assignee

Maximum power demand forecasting method  
Patent #
JP3224641B2
Filed 07/27/1993

Current Assignee

METHOD AND DEVICE FOR LEARNING/FORECASTING IRREGULAR TIME SERIAL DATA USING RECURRENT NEURAL NETWORK, AND WEATHER FORECASTING METHOD  
Patent #
JP2007095046A
Filed 08/31/2006

Current Assignee

FUNCTION APPROXIMATION DEVICE, ENHANCED LEARNING SYSTEM, FUNCTION APPROXIMATION SYSTEM, AND FUNCTION APPROXIMATION PROGRAM  
Patent #
JP2009064216A
Filed 09/06/2007

Current Assignee

Method for training neural networks  
Patent #
CN 101,310,294 A
Filed 11/15/2006

Current Assignee

Method for analyzing and processing experimental data based on artificial neural network  
Patent #
CN 101,814,158 A
Filed 02/20/2009

Current Assignee

10 Claims

1. weather forecasting method based on the BP neural network may further comprise the steps:

Step 1, the original training data matrix of reception and training duration parameters; Step 2, initialization data comprise each neuron output initial value of maximum times, inertial coefficient, hidden layer and output layer of setting learning rate, anticipation error, training, dynamically obtain row matrix column data p0 according to raw data; Step 3, the maximal value maxv (j) that obtains every row training data carry out normalization with minimum value minv (j) back to data and handle, and make raw data standard to 0 between 1; Step 4, obtain the input matrix and the output matrix of training sample according to original training data; Step 5, random initializtion weight matrix wki and wij, wki represent to hide the weight matrix of layer to input layer, and wij representes that input layer arrives the weight matrix of hiding layer; Each neuron of layer, each neuronic output of output layer are hidden in step 6, calculating; Step 7, calculate the error that each output and hidden neuron calculate output, the weights in the network are upgraded in backpropagation; Step 8, repeating step 6, till satisfying end condition, the end condition of this algorithm is an error less than anticipation error or frequency of training greater than maximum set value.


2. the weather forecasting method based on the BP neural network as claimed in claim 1 is characterized in that:

Further comprise; Step 9, the parameter during according to the weight matrix that obtains after the training and training;
Predict as initial input with the last item real data;
To predict the outcome and predict once more as the real data of next day, up to satisfying prediction fate parameter, forecasting process is with step 6;Step 10, the matrix that predicts the outcome that will obtain recover, and the value after the normalization soon returns to actual numerical value.


3. the weather forecasting method based on the BP neural network as claimed in claim 2 is characterized in that:

The said method that value after the normalization is returned to actual numerical value does, Res (i, j)=PredictRes (j, i) * (maxv (j)minv (j))+minv (j), wherein, PredictRes (j, i) unreduced the predicting the outcome of expression, Res (i, j) asreduced the predicting the outcome of expression.


4. the weather forecasting method based on the BP neural network as claimed in claim 1 is characterized in that:
In the said step 3, normalizing use p (i, j)=(p0 (i, j)minv (j))/(maxv (j)minv (j));
Wherein (i j) is the normalizing result of the capable j row of i to p, and (i j) is the historical data of the capable j row of i to p0, and minv (j) is the minimum value of j row, and maxv (j) is the maximal value of j row.

5. the weather forecasting method based on the BP neural network as claimed in claim 1 is characterized in that:
Said original training data is continuous several days maximum temperature and minimum temperature, and said prediction duration is 7 days.

6. the weather forecasting method based on the BP neural network as claimed in claim 1 is characterized in that:
In the step 6, a hiding layer output computing formula is;
O=(e ^{a}e ^{a})/(e ^{a}+ e ^{a}), the output computing formula of output layer is;
O=1/ (1+e ^{a}), wherein a representes neuronic input value.

7. the weather forecasting method based on the BP neural network as claimed in claim 1 is characterized in that:

In the step 7, the Error Calculation function is; If output neuron, then error E rr _{i}=O _{i}(1O _{i}) (T _{i}O _{i}), O _{i}Be the output of output neuron i, T _{i}It is the actual value of this output neuron;
If hidden neuron, then error E rr _{i}=O _{i}(1O _{i}) ∑
_{j}Err _{j}w _{Ij},O _{i}Be the output of hidden neuron i, this neuron has j the output to lower floor, said Err _{j}Be the error of neuron j, w _{Ij}Be the weights between these two neurons;
Said adjustment weights function is;
w _{Ij}=w _{Ij}+ l*Err _{j}* O _{i}, l is a pace of learning.


8. a weather forecasting method is characterized in that, may further comprise the steps:

Step 1 provides N the temperature value of information of capable 2 row, and the said two row temperature values of information are respectively the history value of the highest temperature and the lowest temperature; Step 2, the temperature value of information that said N capable 2 is listed as is normalized to the numerical value between 0 to 1; Step 3 is set up the input and output matrix of training sample, that is, with the 1st to N1 bar data as input matrix, the 2nd to N bar data as output matrix; Step 4 based on said output matrix, utilizes neural network to carry out weather forecasting.


9. weather forecasting method as claimed in claim 8 is characterized in that:
 said step 4 further comprises;
Step 41 trains said neural network; And, Step 42 calculates output with the last item of training sample record as known conditions, and will export the result as known;
Utilize the given data iteration to predict then, obtain beginning to predicting predicting the outcome of duration last unit stage from the last item training data, this moment, 1 real data and n1 bar predicted data were in same interim result set;
Utilize data and weight matrix in the abovementioned interim result set as known, calculate all matrixes that predicts the outcome;Step 43, after the matrix that obtains predicting the outcome, model gets into the last antinormalized stage, and the result data after the normalization soon reverts to normal temperature value.
 said step 4 further comprises;

10. weather forecasting method as claimed in claim 9 is characterized in that:
 among the said step 41, utilize neural network to train further and may further comprise the steps;
Step 411, first random initializtion input layer is to hiding layer, hiding the weight matrix of layer to output layer; Step 412;
Calculate and hide each neuron of layer, each neuronic output of output layer;
Calculate the error of each output neuron and hidden neuron output, said error is neuronic output and the more resulting error of real data, and the weights in the network are upgraded in backpropagation;
When error is accomplished said training step, record weight matrix during less than ultra, the set excessively maximum frequency of training of set anticipation error or frequency of training.
 among the said step 41, utilize neural network to train further and may further comprise the steps;
Specification(s)