With the support of GIS and RS, efforts were made to develop a quantitative analysis model of urban spatial thermal environment based on genetic algorithms back propagation and genetic algorithms.
针对以往空间热环境分析模型的不足，在遥感、地理信息系统支持下，创建了基于人工神经网络和遗传算法的城市热环境非线性定量分析模型。
There are a few training algorithms for parameter estimation of neural networks, in which Back Propagation(BP)algorithm is the typical algorithm for feed-forward multi-layer neural networks.
神经网络参数估计有许多训练算法，BP算法是前向多层神经网络的典型算法，但BP算法有时会陷入局部最小解。
Finally, several example simulations are made to compare our algorithms with traditional error back propagation, simple weight decay, and relative methods in other paper.
最后通过大量实例仿真将它们与纯误差驱动的方法、权退化法、其它文献中的相关方法进行了比较。
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