基于自适应模糊广义回归神经网络的区域火灾数据推理预测  

Reasoning and forecasting of regional fire data based on adaptive fuzzy generalized regression neural network

作　　者：金杉[1,2] 金志刚[1] 

机构地区：[1]天津大学电子信息工程学院,天津300072 [2]天津市河西区公安消防支队信息通信科,天津300222

出　　处：《计算机应用》2015年第5期1499-1504,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61201179)

摘　　要：针对基于反向传播(BP)神经网络和经典概率论及其衍生算法进行火灾损失预测时,存在系统结构复杂、依赖不稳定的探测数据、易陷入局部极小值等缺点,提出一种基于自适应模糊广义回归神经网络(GRNN)的区域火灾数据推理预测算法。在网络输入层使用改进模糊C-聚类算法,对初始数据进行权重修正,减少了噪声和孤立点对算法造成的影响,提高了预测值的逼近精度;引入自适应函数优化GRNN算法,调整迭代收敛的扩展速度、变化步长,找到全局最优解,改善了过早收敛问题,提高了搜索效率。实验结果表明,该算法代入已确定火灾损失数据,解决了依赖不稳定探测数据问题,并且具有良好的泛化能力、非线性逼近能力。While BP neural network,classical theory of probability and its derivative on algorithm were used to fire loss prediction,the system structure is complex,the detection data is not stable,and the result is easy to fall into local minimum,etc. To resolve these troubles, a method of reasoning and forecasting the regional fire data was proposed based on adaptive fuzzy Generalized Regression Neural Network（ GRNN）. The improved fuzzy C-clustering algorithm was used to correct weight for the initial data in network input layer, and it reduced the influence of noise and isolated points on the algorithm, improved the approximation accuracy of the predicted value. The adaptive function optimization of GRNN algorithm was introducd to adjust the expansion speed of the iterative convergence, change the step, and found the global optimal solution. The method was used to resolve the premature convergence problem and improved the search efficiency. While the identified fire loss data is put into the algorithm, the experimental results show that the method can overcome the problem of instable detection data,and has good ability of nonlinear approximation and generalization capability.

关 键 词：自适应 模糊 广义回归神经网络 区域火灾数据 预测 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]