生理过程模糊神经推理的虚拟植物

Virtual plant based on fuzzy neuron Inference of physiology process

提出一种基于模糊神经生理过程推理系统的植物生长模型。该模型基于机器学习理论,从测量的数据中自动学习拟合植物器官生长函数,提取植物生长规律。在植物生长发育过程中,源库器官根据其生长函数响应虚拟环境,并进行生产、分配、利用同化物。同时器官功能部分的变化反馈到结构部分,对表示植物结构信息的L文法字符串进行修改。当生长环境变化时,模型自动调整生长函数的参数和L文法,在环境中进行优化选择,最终形成适应当前虚拟环境的植物。基于辣椒实验仿真表明,该方法能够准确提取植物生长函数和结构规律,逼真地展现植物对生长环境的响应和适应。

A fuzzy neuron inference of physiological process （FNI-PP） based virtual plant growth model is pro posed. Using machine learning theory, the model can automatically learn and fit the plant growth function according to measured data and extract the plant growth rules. During plant growth, the source and sink organs respond the surrounding virtual environment according to its inbuilt growth function, and produce, allocate and consume assimilates as well as update the L-grammar representing the plant structure. The model can automatically adjust parameters of the growth function and the L-grammar to respond the environmental heterogeneity. Cayenne-based simulations show that the model can accurately extract the growth function and the structural pattern of the plant, and vividly demonstrate the response to environment.