所有智能预测模型在MATLAB7.0平台基础上的设计与开发程序。
All intelligent prediction model based on MATLAB7.0 platform, the design and development process.
采用径向基神经网络（RBFNN）在MATLAB环境下建立了混煤软化温度的智能预测模型。
Through the use of a radial-based function neural network(RBFNN) an intelligent forecasting model for the blended-coal softening temperature was set up under MATLAB environment.
本文主要对模糊系统和神经网络技术在模型预测、故障模式分类和智能监测中的应用进行了研究。
In this paper the technologies of model forecasting and the fault model identification and the intelligence monitoring using fuzzy system and neural network are studied.
在用遗传方法对灰色理论建模数据进行全局优化的基础上，建立了预测冻结法施工中外层井壁承受冻结压力发展趋势的智能灰色理论模型。
On the basis of the global optimization of data which is used to build up mold, the artificial intelligent grey theory mold was established to forecast the freeze pressure development trend.
针对铜闪速炉冰铜温度预测问题，提出了一个基于智能集成策略的预测模型。
In order to estimate the matte temperature of copper flash smelter, an estimation model based on intelligent integrated strategy is put forward.
提出了一种新的基于智能化信息处理的建筑工程造价短期预测模型。
A novel short-term forecasting model of construction cost based on intelligent information processing (IIP) technology is presented.
本文提出了一种将多种预测模型与人工智能技术相结合的短时交通流智能预测方法。
The present thesis provides a method to predict the traffic flow in a short period by combining several prediction models and artificial intelligence.
最后应用BP网络原理智能分析模型，开发了承载力预测应用软件。
In the end, applying with the BP intellectual analysis model, develops into a predictable applicable software.
文章实现库存警告与预测模型完美的结合，对推进库存智能化管理有着极其重要的作用。
The article realizes stock warning and estimate model of perfect combination, and make an insignificant effort in promoting the stock intelligent management.
本文针对流程制造企业的管理模式和相应的供应链特点，采用多智能体技术建立基于预测的流程供应链模型。
Firstly this paper analyzes the features of enterprise management and supply chain in process industry, then a forecast based process supply chain model is proposed by multi-agent technology.
因此本文从分析财务预警问题的特点出发，融合了智能软计算的多种方法建立了完整的预测模型。
After analyzing the characteristic of the early warning problem, we merged many kinds of soft computing methods to construct the prediction model.
它是一个智能化的关系，能仿真预测专家进行市场预测的全过程，从信息收集、分析选择模型、实际预测直至对预测的评价。
It stimulates the idea of an expert on forecast, including collecting information, analysing and choosing model, forecasting and evaluating forecast.
第一个分析的问题是堤坝管涌问题，应用人工智能技术建立了管涌预测与判定的模型。
The first problem analyzed is seepage piping in embankment, the artificial intelligent model of prediction and judgment for piping is proposed.
检验结果表明：模型可靠，预测精度高。为冲击地压与人工智能等高新技术的进一步结合奠定了基础，进一步拓宽了神经网络技术的研究领域。
The testing results show that the model is reliable and precise, which forms the combinative basis of rock burst and the artificial intelligence of advanced technologies.
该模型利用RBF神经网络的非线性逼近能力对预测日负荷进行了预测，并采用在线自调整因子的模糊控制对预测误差进行在线智能修正。
The model forecasts the daily load by the nonlinear approaching capacity of the RBF neural network, than corrects the errors by on-line self-tuning factors of fuzzy control.
该模型利用RBF神经网络的非线性逼近能力对预测日负荷进行了预测，并采用在线自调整因子的模糊控制对预测误差进行在线智能修正。
The model forecasts the daily load by the nonlinear approaching capacity of the RBF neural network, than corrects the errors by on-line self-tuning factors of fuzzy control.
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