Improved neural network model based on particle swarm optimization algorithm for data prediction method

A neural network model and particle swarm optimization technology, applied in the field of neural network models, can solve problems such as the difficulty of determining the number of neurons in the hidden layer and the impact of RBF neural network performance

Active Publication Date: 2018-02-27
NORTH CHINA ELECTRIC POWER UNIV (BAODING) +2
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Problems solved by technology

[0005] The present invention provides an improved neural network model based on particle swarm optimization algorithm for data prediction method, which overcomes the above-mentioned deficiencies in the prior art, and can effectively solve the problem that the number of neurons in the hidden layer of the RBF neural network model is difficult to determine. Problems that have a large impact on neural network performance

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  • Improved neural network model based on particle swarm optimization algorithm for data prediction method
  • Improved neural network model based on particle swarm optimization algorithm for data prediction method
  • Improved neural network model based on particle swarm optimization algorithm for data prediction method

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Embodiment 1

[0050] Embodiment 1: as attached figure 1 As shown, the improved neural network model based on the particle swarm optimization algorithm is used in the data prediction method, and the following steps are carried out:

[0051] Step 1: Representation of the data sample, using variables Denotes the i-th sample, using Indicates the t-th component of the i-th sample;

[0052] Step 2: Data preprocessing, constructing a training sample set X based on data samples train ={x 1 ,x 2 ,...,x N} and test sample set X test ={x 1 ,x 2 ,...,x K};

[0053] Step 3: Initialize the parameters of the RBF neural network, and determine the number N of neurons in the input layer according to the dimension M of the training sample set input =M, determine the number of neurons in the output layer N according to the dimensionality of the data to be predicted output =P;

[0054] Step 4: Use the binary particle swarm optimization algorithm to determine the number of hidden layer neurons and...

Embodiment 2

[0064] Step 14: Use the RBF neural network model for data prediction, use the test sample set as the input sample of the RBF neural network model, and use the established RBF neural network model to predict unknown data. The hidden layer of the RBF network model uses Radial basis kernel function, the formula of radial basis kernel function is x i Indicates the input sample, c i Indicates the center of the kernel function of the i-th neuron in the hidden layer, σ indicates the width of the kernel function, using the formula Calculate the predicted output value of the RBF neural network model, that is, the predicted value for unknown data. Embodiment 2: The difference from the above embodiment is that step 4 includes the following sub-steps:

[0065] Sub-step 401: Binary particle swarm optimization algorithm parameter initialization, the number of initialized particle swarms is N swarm Represents; randomly initialize the position of the particle swarm with said, among the...

Embodiment 3

[0077] Embodiment 3: The difference from the above embodiment is that step 8 includes the following sub-steps:

[0078] Sub-step 801: construct a kd tree (k-dimensional tree) according to the k-nearest neighbor algorithm (k-nearest neighbor, k-NN) and the values ​​of each dimension of the particles in the particle swarm;

[0079] Sub-step 802: According to the kd tree search principle, find the k nearest neighbor particles of each particle, i.e. top(k) particles;

[0080] Sub-step 803: According to the fitness function values ​​of the k nearest neighbor particles obtained from the search, determine the optimal particle p in the neighborhood of each particle lbest ;

[0081] Sub-step 804: k=k+1, that is, the neighborhood of each particle expands the range of one particle during the next iteration, until the value of k is equal to the size of the particle swarm, k=N l-swarm .

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Abstract

The invention relates to the technical field of computer application engineering, in particular to a method for using an improved neural network model based on particle swarm optimization for data prediction. The method includes the steps of firstly, expressing data samples; secondly, pre-processing data; thirdly, initiating the parameters of an RBF neural network; fourthly, using the binary particle swarm optimization to determine the number of neurons of a hidden layer and the center of the radial basis function of the hidden layer; fifthly, initiating the parameters of the local particle swarm optimization. By the method for using the improved neural network model based on particle swarm optimization for data prediction, the number of the neurons of the hidden layer of the RBF neural network model can be determined easily, RBF neural network performance is improved, and data prediction accuracy is increased. In addition, the improved neural network model based on particle swarm optimization is low in model complexity, high in robustness and good in expandability.

Description

technical field [0001] The invention relates to the technical field of computer application engineering, and relates to a data prediction method based on an improved neural network model based on a particle swarm optimization algorithm. Background technique [0002] The RBF (radial basis function) neural network model is a self-organizing and self-learning structured prediction method. It can establish a structured prediction model through self-organizing and self-learning methods based on historical detection data within a period of time. The model can describe and track the variation of data to the greatest extent, and make predictions with the greatest probability for unknown data. This model is widely used due to its simple structure, small parameters, and better performance. [0003] However, in engineering applications, the number of neurons in the hidden layer of the RBF neural network model has a greater impact on the performance of the network. If the number of neu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/02
Inventor 李国栋刘琳宋志新王晓磊李凯黄琳华
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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