基于小生境技术的火灾图像识别算法

火灾图像识别是火灾探测研究的重要组成部分。随着人工智能技术应用的不断深入,遗传算法和神经网络也被应用到火灾图像识别中。针对目前的遗传神经网络火灾图像识别算法、网络结构不易确定的问题,本文提出了一种基于小生境技术的火灾图像识别算法,即依据火灾图像识别的特点,建立了多层前向神经网络模型,模型的输入、输出层节点数确定,隐含层数、隐含层节点数待定;然后对网络结构和权值、阈值编码,分别采用小生境技术和传统遗传算法训练神经网络模型。实验结果显示,本算法可有效减少进化的代数,加快训练的过程,最后采用训练好的神经网络模型对火灾图像进行识别,取得了较好的效果。

A Fire Image Recognition Algorithm Based on the Niche Technology

Fire image recognition is an important part of the fire detection research. With a continuous deep development of the artificial intelligence application technology, genetic algorithms and neural networks have also been applied to fire image recognition. Aiming at the problem that network structure is not easy to determine in fire image recognition based on genetic and neural networks, a fire image recognition algorithm based on genetic algorithms by the niche technology and neural networks is proposed. According to the characteristics of fire image recognition, the algorithm firstly establishes the multi layer front neural network model, determines the number of input and output level nodes, and undecides the number of the concealed levels and the conceal level nodes in the model, and then codes the network structure and weight value, threshold value, and trains the network model using genetic algorithms by the niche technology and the classic genetic algorithms. The experimental results show that the algorithm can reduce the number of evaluation generations effectively and speed up the training process. And finally we perform fire image recognition by the trained neural network model, and obtain better results.