Evaluation of chaos in plant response and its identification using neural networks

摘要

Plant responses are characterized by complexity and uncertainty. In this study, a neural-network identification technique, taking the concept of chaos into consideration, is discussed, aiming at a more precise identification. In the method, the complexity of the data (diurnal change in the net photosynthetic rate of the plant grown in greenhouse is quantitatively evaluated by introducing the concept of chaos before performing identification. Here, attractor of the data is described in the phase space, and the fractal dimension) is quantitatively evaluated by introducing the concept of chaos before performing identification. Here, attractor of the data is described in the phase space, and the fractal dimension is calculated to measure the irregularity.