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Physiomechanic Control of Cell Division Rate at Plant Root Tip

植物根尖细胞分裂速率的物理力学控制

基本信息

批准号：
02660258
负责人：
MURASE Haruhiko
金额：
$ 1.28万
依托单位：
University of Osaka Prefecture
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1990
资助国家：
日本
起止时间：
1990 至 1991
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-02660258/
关键词：
physiomechanics digital control theory region of cell division fuzzy systems water potential neural networks

项目摘要

In protected cultivation, fairy high level of control technology for plant environment has been developed. Plant factory is a good example of protected cultivation under intensive controlled environment. In agricultural engineering field of study, significant accumulation of good study resofts in instrumentation, measurement and control technology in relation to plant physiology enable us to seek possibility of development of new technology. The overall objective of this research was to conduct theoretical and experimental study to develop new physical control technology that can be implemented to control plant physiology directly. The objective can be achieved through application of physiomechanics theory. The followings are individual objectives of this research. The first effort was to Identify the physiological system including cell division of root tip using fuzzy linear system theory and diSSftal control theory. The second effort was to develop control technology that can control … More rate of cell division at root tip In hydroponic cufture. The control system for governing rate of cell division at root tip was developed using water potential control and ultra sonic wave.The actual achievement of this work was to succeeded to identify the system of cell division, plant physiology and metabolism using neural networks. The hierarchical neural network can be used to model biological systems such as plant growth, photosynthesis, evapotranspiration, etc. The development of back-propagation algorithm for neuron training has made h possible to use the layered network for simulating such non-linear systems. Modeling of such biological systems using the neural network often requires large number of layers and units in the network architecture because of the complexity of the system. The back propagation algorithm, however, often fails to achieve satisfactory identification of the system in the sense that output error minimization characteristics of the steepest descent scheme of the back propagation algorithm does not fit the problems involving large number of estimation parameters(synapse weights). Simulation of growth of radish sprouts under influence of changes in temperature and concentration of nutrient solution was attempted by two different neural network models, i. e., Kalman fitter model and back propagation model. Less

在设施栽培中，植物环境控制技术已发展到相当高的水平。植物工厂是集约化控制环境下设施栽培的典范。在农业工程研究领域中，与植物生理有关的仪器、测量和控制技术的研究成果的大量积累，使我们能够寻求新技术发展的可能性。本研究的总体目标是进行理论和实验研究，以开发新的物理控制技术，可以实施直接控制植物生理。应用生理力学理论可以达到这一目的。以下是本研究的个别目标。本文首先应用模糊线性系统理论和离散控制理论对包括根尖细胞分裂在内的生理系统进行了辨识。第二项努力是开发控制技术， ...更多信息水培条件下根尖细胞分裂速率。利用水势控制和超声波技术，建立了植物根尖细胞分裂速率控制系统，其实际成果是成功地利用神经网络对细胞分裂、植物生理和代谢系统进行了辨识。分层神经网络可用于模拟植物生长、光合作用、蒸散等生物系统。神经元训练的反向传播算法的发展使分层网络用于模拟这类非线性系统成为可能。由于系统的复杂性，使用神经网络对这种生物系统进行建模通常需要网络架构中的大量层和单元。然而，反向传播算法，往往无法实现令人满意的识别的系统的意义上，输出误差最小化特性的最陡下降方案的反向传播算法不适合的问题，涉及大量的估计参数（突触权重）。利用两种不同的神经网络模型模拟了温度和营养液浓度变化对萝卜幼苗生长的影响。例如，卡尔曼拟合模型和反向传播模型。少