?About the author: A Matlab simulation developer who loves scientific research. He cultivates his mind and improves his technology simultaneously. For cooperation on MATLAB projects, please send a private message.
Personal homepage: Matlab Research Studio
Personal credo: Investigate things to gain knowledge.
For more complete Matlab code and simulation customization content, click
Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
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
?</code><code>% This function initializes the first population of search agents</code><code>function Positions=initialization(SearchAgents_no,dim,ub,lb)</code><code>?</code><code>Boundary_no= size(ub,2); % number of boundaries</code><code>?</code><code>% If the boundaries of all variables are equal and user enter a signle</code><code> code><code>% number for both ub and lb</code><code>if Boundary_no==1</code><code> Positions=rand(SearchAgents_no,dim).*(ub-lb) + lb;</code><code>end</code><code>?</code><code>% If each variable has a different lb and ub</code><code>if Boundary_no>1</code><code> for i=1:dim</code><code> ub_i=ub(i);</code><code> lb_i=lb(i);</code><code> Positions(:,i)=rand( SearchAgents_no,1).*(ub_i-lb_i) + lb_i;</code><code> end</code><code>end
Operation results
? References
[1] Yin Lisheng, Liu Pan, Sun Shuangchen, et al. Optimizing the traffic flow combination prediction model of two-way gated cyclic units based on complementary set empirical mode decomposition and improved sparrow search algorithm [J]. Journal of Electronics and Information, 2022, 44:1 -10.DOI:10.11999/JEIT221172.
[2] Zuo Siyu, Zhao Qiang, Zhang Bing, et al. Ship motion attitude prediction based on VMD-SSA-GRU [J]. Ship Science and Technology, 2022, 44(23):60-65.
[3] Wan Dingsheng, Zhu Hainan, Liu Xinyue, et al. Construction method of hydrological prediction model based on regularization and adaptive genetic algorithm: CN202010876835.9[P].CN112015719A[2023-09-10].
[4] Duan Wanling. Analysis and prediction of the relationship between RMB daily exchange rate value and basic economic variables[J].[2023-09-10].
Some theories are quoted from online literature. If there is any infringement, please contact the blogger to delete it
Follow me to receive massive matlab e-books and mathematical modeling materials
Complete code and data acquisition via private message and real customization of paper data simulation
1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation
, Online optimization of UAV safety communication trajectory
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow Crowd evacuation Virus spread Crystal growth Fire spread
9 Radar aspect
Kalman filter tracking, track correlation, track fusion, state estimation
The knowledge points of the article match the official knowledge files, and you can further learn related knowledge. Algorithm skill tree Home page Overview 52,255 people are learning the system