Neural network structure diagram.
<div><p>While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) alg...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852020702859755520 |
|---|---|
| author | Jining Wang (3369305) |
| author2 | Huabin Ji (21262084) Lei Wang (6656) |
| author2_role | author author |
| author_facet | Jining Wang (3369305) Huabin Ji (21262084) Lei Wang (6656) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jining Wang (3369305) Huabin Ji (21262084) Lei Wang (6656) |
| dc.date.none.fl_str_mv | 2025-05-07T17:43:57Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0322821.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Neural_network_structure_diagram_/28948846 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Science Policy Plant Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified test set achieved particle swarm optimization dataset comprising 1 bp neural network forecast housing prices hand house prices forecasting house prices traditional genetic algorithms forecasting prices hand homes xlink "> work shows research provides premature convergence often suffer dimensional data building upon adversely affects 9 %. 824 transactions |
| dc.title.none.fl_str_mv | Neural network structure diagram. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops a GA-PSO-BP neural network model through the integration of the BP neural network. Building upon this foundation, the study considers several pivotal factors affecting housing prices and employs a dataset comprising 1,824 transactions of second-hand homes from 2023 to 2024, gathered from Lianjia.com, to forecast housing prices in China. This work shows that the GA-PSO-BP neural network model demonstrates exceptional forecasting performance when dealing with complex and high-dimensional data, significantly minimizing forecasting errors. The test set achieved an RMSE of 0.786 and a MAPE of 8.9%. Its effectiveness in forecasting prices of second-hand houses notably surpasses that of a BP neural network model optimized by a single algorithm. This research provides more accurate forecasts of second-hand house prices in rapidly growing urban areas such as Guangzhou, thus providing essential insights for investors contemplating real estate investment.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_3206034dfbde8af16e112a066ccd58aa |
| identifier_str_mv | 10.1371/journal.pone.0322821.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28948846 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Neural network structure diagram.Jining Wang (3369305)Huabin Ji (21262084)Lei Wang (6656)NeuroscienceScience PolicyPlant BiologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtest set achievedparticle swarm optimizationdataset comprising 1bp neural networkforecast housing priceshand house pricesforecasting house pricestraditional genetic algorithmsforecasting priceshand homesxlink ">work showsresearch providespremature convergenceoften sufferdimensional databuilding uponadversely affects9 %.824 transactions<div><p>While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops a GA-PSO-BP neural network model through the integration of the BP neural network. Building upon this foundation, the study considers several pivotal factors affecting housing prices and employs a dataset comprising 1,824 transactions of second-hand homes from 2023 to 2024, gathered from Lianjia.com, to forecast housing prices in China. This work shows that the GA-PSO-BP neural network model demonstrates exceptional forecasting performance when dealing with complex and high-dimensional data, significantly minimizing forecasting errors. The test set achieved an RMSE of 0.786 and a MAPE of 8.9%. Its effectiveness in forecasting prices of second-hand houses notably surpasses that of a BP neural network model optimized by a single algorithm. This research provides more accurate forecasts of second-hand house prices in rapidly growing urban areas such as Guangzhou, thus providing essential insights for investors contemplating real estate investment.</p></div>2025-05-07T17:43:57ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0322821.g001https://figshare.com/articles/figure/Neural_network_structure_diagram_/28948846CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/289488462025-05-07T17:43:57Z |
| spellingShingle | Neural network structure diagram. Jining Wang (3369305) Neuroscience Science Policy Plant Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified test set achieved particle swarm optimization dataset comprising 1 bp neural network forecast housing prices hand house prices forecasting house prices traditional genetic algorithms forecasting prices hand homes xlink "> work shows research provides premature convergence often suffer dimensional data building upon adversely affects 9 %. 824 transactions |
| status_str | publishedVersion |
| title | Neural network structure diagram. |
| title_full | Neural network structure diagram. |
| title_fullStr | Neural network structure diagram. |
| title_full_unstemmed | Neural network structure diagram. |
| title_short | Neural network structure diagram. |
| title_sort | Neural network structure diagram. |
| topic | Neuroscience Science Policy Plant Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified test set achieved particle swarm optimization dataset comprising 1 bp neural network forecast housing prices hand house prices forecasting house prices traditional genetic algorithms forecasting prices hand homes xlink "> work shows research provides premature convergence often suffer dimensional data building upon adversely affects 9 %. 824 transactions |