摘要
提出一种基于模糊神经网络的植物建模方法,将测量的植物生理数据作为模糊神经网络的输入,自动学习拟合植物器官生长函数,提取生长规则。由同化物驱动植物生长发育,对虚拟器官的属性进行修改,将生理部分的变化反馈到结构部分。当虚拟环境变化时,模型响应环境变化,自动调整生长函数的参数和生长规则,使植物趋向于有利生长环境。实验结果表明,该方法能够准确提取植物生长函数和生长规则,对植物生长进行逼真的模拟。
A plant model based on fuzzy neural network is proposed. Measured physiological data of plant are used as the input of fuzzy neural networks. The model can automatically learn and fit plant growth function and extract rules of plant growth. The plant growth is driven by carbohydrate. The structural attributes of virtual organs are modified by physiological process. The model can automatically adjust parameters of growth function and growth rules in response to environmental heterogeneity. Experimental results show that this method can accurately extract the growth function and growth rules, vividly simulate the growth of plant.
出处
《计算机工程》
CAS
CSCD
2012年第6期273-275,共3页
Computer Engineering
