The results of modeling show that the improved method is useful for establishing the modeling of complex systems.
建模结果表明：改进方法对建立一般复杂过程的数学模型是有效的。
Artificial neural network (ANN) is widely applied to the modeling of complex systems, and now it has become a common modeling method in the study of materials science.
人工神经网络技术广泛应用于复杂系统的建模中，已成为材料科学研究中常用的建模方法。
Modeling technology plays an important role in design, development, integration and test of complex software systems.
建模技术在复杂软件系统的设计、开发、集成和测试中起着重要的作用。
Warship maintains system is a complex systems, traditional method of modeling is difficult in displaying its action process or evaluating its support ability wartime.
舰船维修系统是一个复杂系统，传统的建模方法难以表现其行为过程，并正确评估其战时保障能力。
The Unified modeling Language (UML) has become a de-facto industrial standard for object-oriented modeling of large, complex systems.
统一建模语言(uml)已经成为面向对象建模事实上的工业标准，用于大型复杂系统。
To improve the modeling efficiency and reuse of models, subsystem modeling techniques for complex multi-body systems are addressed.
针对多体系统建模效率低、模型重用困难等问题，研究了复杂多体系统模型的子系统建模技术。
The paper introduces a kind of adaptive neural-fuzzy inference systems (ANFIS) based on T-S model to deal with the modeling problem of the complex soda carbonization process.
针对纯碱碳化过程的复杂建模问题，提出基于T-S模型的自适应神经模糊推理系统（ANFIS）的建模方法。
A self organizing method for modeling of a class of forecasting models is proposed. The model is used to forecast the occurrence of rare events in complex systems.
提出了一类预测模型的一种自组织方法，这类模型适用于预测复杂系统中稀异事件的发生。
The modeling approach can describe clearly the inter-dependent relationship between system faults and test symptoms, and reflect the hierarchical structure of complex systems.
该建模方法能清晰地刻画系统故障与测试间的关联程度，反映复杂系统的层次结构关系。
Traditional modeling technologies are restricted in applications of actual large-scale complex industrial systems. To some extent, artificial intelligence overcomes the problem.
传统建模方法在实际大规模复杂工业系统应用中受到限制，人工智能在一定程度上克服了这个问题。
Effective modeling of complex concurrent systems requires a formalism that can capture essential properties such as nondeterminism, synchronization and parallelism.
对复杂的并发系统进行有效建模要求有一套形式化体系，由它能获取系统的本质特性，如不确定性、同步性和并发性。
The deterministic learning theory will provide a new approach to data-based modeling, recognition, control of complex processes and systems.
本文表明确定学习可以为时态数据挖掘的研究提供新的途径，并为基于数据的建模与控制等问题提供新的研究思路。
The deterministic learning theory will provide a new approach to data-based modeling, recognition, control of complex processes and systems.
本文表明确定学习可以为时态数据挖掘的研究提供新的途径，并为基于数据的建模与控制等问题提供新的研究思路。
应用推荐