SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction

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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system

Signal processing Image processing Path planning Cellular automaton Drone

Content introduction

As the global demand for renewable energy continues to grow, wind energy, as a clean and renewable form of energy, has gradually received widespread attention. However, due to the instability and uncontrollability of wind energy, wind power power prediction has become an important research field in the wind power industry. Accurately predicting wind power power can help grid managers better dispatch power resources and improve the reliability and stability of the power system.

In the past few decades, artificial intelligence technology has been widely used in wind power prediction. Among them, BP neural network is a commonly used prediction model with strong nonlinear modeling capabilities. However, BP neural network easily falls into the local optimal solution during the training process, resulting in low prediction accuracy. To solve this problem, researchers have proposed many optimization algorithms to improve the performance of BP neural networks.

The slime mold algorithm (SMA) is an emerging optimization algorithm inspired by the biological world. It simulates the growth and reproduction process of slime molds in nature, and finds the optimal solution through information exchange between slime molds. The SMA algorithm has global search capabilities and strong robustness, and can effectively prevent the BP neural network from falling into the local optimal solution.

In the BP neural network optimized based on the slime mold algorithm, it is first necessary to determine the structure and parameters of the neural network. Usually, the structure of neural network includes input layer, hidden layer and output layer. The input layer receives meteorological data such as wind speed and wind direction, the hidden layer converts the input signal into a nonlinear output through the activation function, and the output layer outputs the prediction result of wind power power. After determining the structure of the neural network, the SMA algorithm needs to be used to optimize the weights and thresholds of the neural network.

The optimization process of the SMA algorithm includes steps such as initialization, slime migration, slime reproduction, and slime update. During the initialization phase, parameters such as the position and viscosity of the slime mold are randomly generated. Then, based on the interaction between slime molds, slime mold migration and reproduction operations are performed to find a better solution. Finally, in the slime mold update phase, the location and viscosity of the slime mold are updated based on the fitness value of the slime mold. Through multiple iterations, the SMA algorithm can gradually optimize the weights and thresholds of the BP neural network and improve the accuracy of wind power power prediction.

Experimental results show that the BP neural network optimized based on the slime mold algorithm has high accuracy and stability in wind power power prediction. Compared with the traditional BP neural network, the optimized neural network can better capture the characteristics and changing trends of wind energy and improve the prediction accuracy. In addition, the SMA algorithm also has fast convergence speed and strong robustness, and can adapt to different wind power power prediction problems.

In summary, the SMA optimized BP neural network based on the slime mold algorithm is an effective wind power power prediction method. It can overcome the local optimal solution problem of BP neural network and improve the prediction accuracy and stability. In the future, we can further explore the application of the slime mold algorithm in other fields and combine it with other optimization algorithms to further improve the performance of wind power power prediction.

Part of the code

%_______________________________________________________________%
% Gray Wolf Optimizer (GWO) source codes version 1.0 %
% %
% Developed in MATLAB R2011b(7.13) %
% %
% Author and programmer: Seyedali Mirjalili %
% %
% e-Mail: [email protected] %
% [email protected] %
% %
% Homepage: http://www.alimirjalili.com %
% %
% Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis %
% Gray Wolf Optimizer, Advances in Engineering %
% Software, in press, %
% DOI: 10.1016/j.advengsoft.2013.12.007 %
% %
%____________________________________________________________%

% 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

Running results

SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction_path planning

SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction _Neural Network_02Edit

SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction_path planning_03

SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction _Neural Network_04Edit

SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction_path planning_05

SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction _Drone_06
edit

References

[1] Fan Yuanyuan, Meng Difei, Sang Yingjun, et al. Elevator fault prediction method based on improved slime mold algorithm optimizing Elman neural network. CN202211341256.X[2023-09-22].

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1 Improvement and application 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 application
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
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
9 Radar
Kalman filter tracking, track correlation, track fusion