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This study provides quantitative support for analyzing current fire communication command systems and enabling their iterative upgrades. A four-level efficacy evaluation index system for brigade-level fire command communication systems was constructed, based on fire communication command system design specifications. This system assessed three key dimensions: operational support capability, data service capability, and communication assurance capability. An IPSO-BP-based system efficacy evaluation method was proposed, building upon BP neural network algorithm. Parameters were optimized using IPSO algorithm. Sample data were acquired through a combination of expert scoring and the Analytic Hierarchy Process (AHP). Principal Component Analysis (PCA) was applied for dimensionality reduction. Simulation comparisons were conducted using three distinct models: BP neural network, PSO-BP neural network, and IPSO-BP neural network. Results demonstrate that IPSO-BP neural network model achieves the fastest convergence speed. Its mean square error decreases by 75.71% compared to BP neural network model and by 45.96% compared to PSO-BP neural network model, representing the lowest error value among the three models. Furthermore, IPSO-BP model reasonably and accurately evaluates brigade-level fire communication command system efficacy, demonstrating considerable generalizability.

, authors=Zhenjiang YU, authorsList=Zhenjiang YU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1282032310614737099, articleId=1282032303455060166, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于IPSO-BP的消防通信指挥系统效能评价, columnId=1149735801837241068, journalTitle=中国安全科学学报, columnName=安全科学理论与安全系统科学, runingTitle=null, highlight=null, articleAbstract=

为实现消防通信指挥系统的现状研判与迭代升级的量化支撑，基于消防通信指挥系统设计规范，从业务支撑能力、数据服务能力、通信保障能力3个方面构建支队级消防指挥通信系统4级效能评价指标体系；在反向传播（BP）神经网络算法的基础上，通过改进粒子群优化（IPSO）算法优化参数，提出基于IPSO-BP的系统效能评价方法；采用专家打分与层次分析法（AHP）结合的方式获取样本数据，经主成分分析（PCA）方法降维后，分别基于BP神经网络、PSO-BP神经网络、IPSO-BP神经网络这3个模型开展仿真对比。结果表明：IPSO-BP神经网络模型的收敛速度最快，其均方误差相比于BP神经网络模型降低了75.71%，相较于PSO-BP神经网络模型降低了45.96%，为三者中的最小值；IPSO-BP模型能够合理精准地评价支队级消防通信指挥系统效能，具有一定的普适性。

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于振江（1988—），男，河北张家口人，硕士，工程师，主要从事消防管理与技术方面的研究。E-mail: yuzhenjiang@ruc.edu.cn。

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于振江（1988—），男，河北张家口人，硕士，工程师，主要从事消防管理与技术方面的研究。E-mail: yuzhenjiang@ruc.edu.cn。

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