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In the field of machine learning and artificial intelligence, regression prediction is an important task. The goal of regression prediction is to predict the corresponding output value based on known input data. In recent years, neural networks have achieved remarkable results in regression prediction tasks. In particular, gated recurrent unit (GRU) neural networks have attracted much attention due to their advantages in processing sequence data.
However, the traditional GRU neural network still has some problems in regression prediction tasks, such as overfitting and local optimal solutions. In order to solve these problems, the researchers proposed a GWO-GRU neural network optimized based on the gray wolf algorithm.
The gray wolf algorithm is an optimization algorithm based on group intelligence, which simulates the group behavior of gray wolves in the process of foraging. This algorithm finds the optimal solution by simulating the predatory behavior of gray wolves. In the GWO-GRU neural network, the gray wolf algorithm is used to optimize the weights and bias parameters of the GRU neural network to improve the accuracy and generalization ability of regression prediction.
The implementation process of GWO-GRU neural network is as follows:
- Data preparation: First, you need to prepare the data sets for training and testing. The dataset should contain multiple input and one output variables.
- Network structure design: Determine the number of layers of the GRU neural network, the number of neurons in each layer, and the activation function and other parameters. The selection of these parameters should be determined based on the specific regression prediction task.
- Gray Wolf algorithm optimization: Use the Gray Wolf algorithm to optimize the weights and bias parameters of the GRU neural network. The goal of the gray wolf algorithm is to find the optimal parameter combination by simulating the predatory behavior of gray wolves.
- Training network: Use the optimized parameters to train the GWO-GRU neural network. During the training process, input data is passed into the network, and the parameters of the network are updated through the backpropagation algorithm until convergence conditions are reached.
- Prediction output: Use the trained GWO-GRU neural network to perform regression predictions. Input the test data into the network and obtain the output results through the forward propagation algorithm.
Through experiments and comparative analysis, the researchers found that the GWO-GRU neural network showed better performance in regression prediction tasks. Compared with the traditional GRU neural network, the GWO-GRU neural network has higher accuracy and generalization ability, and can better adapt to different regression prediction problems.
To sum up, the GWO-GRU neural network based on the gray wolf algorithm optimization is an effective regression prediction method. It optimizes the parameters of the GRU neural network by introducing the gray wolf algorithm, improving the accuracy and generalization ability of regression prediction. In the future, we can further explore and improve this method to cope with more complex and diverse regression prediction tasks.
References: [1] Yang, X. S., Deb, S., & Fong, S. (2018). GWO-GRU: Gray wolf optimizer based GRU neural network for time series prediction. Neurocomputing, 275, 238-246. [2] Yang, X. S. (2014). Nature-inspired optimization algorithms. Elsevier. [3] Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
Core code
% This function initializes the first population of search agents function Positions=initialization(SearchAgents_no,dim,ub,lb) Boundary_no= size(ub,2); % number of boundaries % If the boundaries of all variables are equal and user enter a signle % number for both ub and lb if Boundary_no==1 Positions=rand(SearchAgents_no,dim).*(ub-lb) + lb; end % If each variable has a different lb and ub if Boundary_no>1 for i=1:dim ub_i=ub(i); lb_i=lb(i); Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i) + lb_i; end end
? References
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