?About the author: A Matlab simulation developer who loves scientific research. He cultivates his mind and improves his technology simultaneously.
For code acquisition, paper reproduction and scientific research simulation cooperation, 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
Parking space prediction has always been an important issue in urban traffic management and parking lot management. With the acceleration of urbanization, the number of vehicles continues to increase, and the demand for parking spaces is also increasing. Therefore, how to effectively predict the demand for parking spaces has become an urgent problem to be solved. The parking space prediction algorithm based on genetic algorithm optimization of BP neural network has become a new solution, which can more accurately predict parking space demand, improve parking lot utilization, and reduce traffic congestion and air pollution.
First, let us understand the genetic algorithm and BP neural network. Genetic algorithm is an optimization method that simulates natural selection and genetic mechanisms. It searches for the optimal solution by simulating the biological evolution process. The BP neural network is a common artificial neural network model that achieves data learning and prediction by continuously adjusting the connection weights between neurons.
The parking space prediction algorithm process based on genetic algorithm optimization of BP neural network is roughly divided into the following steps:
-
Data collection and preprocessing: First, it is necessary to collect historical data of the parking lot, including vehicle entry and exit time, parking duration, number of parking spaces and other information. Then the data is preprocessed, including data cleaning, feature extraction and data standardization.
-
Genetic algorithm optimization BP neural network model construction: Use the collected historical data to build a BP neural network model, and use genetic algorithms to optimize it. The genetic algorithm adjusts the connection weights and biases of the neural network through continuous iteration, so that the neural network can more accurately fit the changing laws of parking demand.
-
Model training and verification: Divide the collected historical data into a training set and a test set, use the training set to train the optimized BP neural network model, and then use the test set to verify the model and evaluate its prediction accuracy and generalization ability.
-
Model application and optimization: Apply the trained model to actual parking lot management, monitor vehicle flow and parking demand in real time, and continuously optimize model parameters to adapt to changes in parking demand.
Through the above process, the parking space prediction algorithm based on genetic algorithm optimization of BP neural network can more accurately predict parking demand, improve parking lot utilization, and reduce traffic congestion and air pollution. This algorithm has achieved good results in practical applications, bringing new solutions to urban traffic management and parking lot management. It is hoped that this algorithm can be further improved and promoted in the future to provide better solutions to urban traffic and parking problems.
Part of the code
tic %This function indicates the start of timing</code><code>clear all;</code><code>clc; </code><code>%original data</code><code>% A = [ 44;34;25;42;37;32;43;25;36;30;35;41;</code><code>% 31;17; 22; 27; 18 ;21 ;33 ;27 ;16; 26; 24; 22;</code><code>% 20;18;19;12;21; 8;14;12;14; 20;8;12;</code><code>% 15;21; 23;13;28;13;23;21;15;28;12;25];</code><code>% B = [40; 38;33;30;28;23;31;27;35; 42; 33 ;41;</code><code>% 33;26;29;30;26;23;23;27;27;24;17;32;</code><code>% 18; 19; 24; 20 ;10 ;10 ;16 ;17 ;21; 12 ;12; 15;</code><code>% 23 ;10; 28 ;11; 23 ;19 ;13; 27 ;9; 26 ;14; 18]; </code> <code> a = [44 31 20 15 23 11 23 37 34 17 18 13 25 38 22 19 23 18 26 37 42 13 25 25 33 37 18 21 28 21 10 22 29 32 21 8 13 15 16 19 25 43 33 14 23 21 20 16 22 25 27 12 21 25 13 14 22 36 16 14 15 18 11 14 25 30 26 20 28 16 19 1 6 29 35 24 8 12 23 18 19 33 41 22 12 25 23 11 23 37]; </code> <code> B = [40 33 18 23 11 20 24 38 38 26 10 11 14 17 33 28 11 20 23 25 30 10 12 12 12 12 18 40 28 26 10 23 14 20 17 26 23 23 10 19 16 13 23 17 31 23 16 13 18 18 14 34 27 27 17 27 20 14 23 37 35 27 21 9 22 17 1 6 19 42 24 12 26 24 16 19 23 33 17 12 14 26 15 22 40 41 32 15 18 27 18 14 29];</code><code>%The measured data to be predicted</code><code>% E = [40; 38;28; 36;30;31;35;29;23;29;39;40;</code><code>% 24;18;14;15;20;26;31;32;28;22;17;14; </code><code>% 16 ;16 ;17 ;16 ;9 ; 19 ;13 ;5 ;13 ;19 ;9 ;6; </code><code>% 22 ;12; 26 ;12; 22 ; 19; 14 ;25; 11 ;24; 15 ;18];</code><code>C = [40 24 16 22 10 11 22 14 38 18 16 12 5 15 20 37 28 14 17 26 6 19 16 18 36 15 16 12 11 17 14 30 30 20 9 22 19 13 13 27 31 26 19 19 25 11 15 20 35 31 13 14 27 14 18 36 29 32 5 15 23 18 22 14 23 28 13 11 16 18 23 38 29 22 19 24 9 14 22 17 39 17 9 15 5 11 19 32 40 14 6 18 7 14 16 25];</code><code>% sequence p0</code><code>p0 = (A + B) / 2;
Operation results
References
[1] Lu Daoyu. Application of BP neural network optimized by genetic algorithm in rainfall prediction [D]. Nanchang Institute of Technology [2023-11-07]. DOI: CNKI: CDMD: 2.1016.056301.
[2] Mo Rong, Tian Guoliang, Sun Huibin. Application of BP neural network based on genetic algorithm optimization in roughness prediction [J]. Mechanical Science and Technology, 2015(5):4.DOI:10.13433/j.cnki. 1003-8728.2015.0515.
[3] Huang Yonghui. Application of intelligent algorithm based on neural network in coke quality prediction[D]. Shenyang Ligong University[2023-11-07].DOI:CNKI:CDMD:2.1015.510401.
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
Private message complete code, paper reproduction, journal cooperation, paper tutoring and scientific research simulation customization
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 site 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 aspect
Kalman filter tracking, track correlation, track fusion
The knowledge points of the article match the official knowledge files, and you can further learn related knowledge. Algorithm skill tree Home page Overview 57426 people are learning the system