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
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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.

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