Trainable Finite Element Neural Network and Intelligent Imaging Device for Pattern Recognition of Biological Object

用于生物对象模式识别的可训练有限元神经网络和智能成像装置

• 批准号: 04660269
• 负责人: MURASE Haruhiko
• 金额: $ 1.34万
• 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: 1992
• 资助国家: 日本
• 起止时间: 1992 至 1993
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-04660269/
• 关键词: FINITE ELEMENT NEURAL NETWORK; PATTERN RECOGNITION; FUNCTIONAL DEVICE; NEURAL NET; IMAGING DEVICE

The finite element neural network (FENN) was appied to a non invasive technique to monitor the plant water status of greenhouse-grown chrysanthemums. The governing differential equation (Poisson's equation) was utilized for neural information processing. The solution of the Poisson's equation was obtained using the finite element technique. A Kalman filter was used as a learuing algorithm of the FENN.It was demonstrated as a practical example of the FENN applications that the FENN provided a means of estimating the leaf water potentials (correlated outputs of the FENN) of a greenhouse-grown chrysanthemum from the digital image data of its leaf (correlated inputs of the FENN).Artificial intelligence is concerned with developing software systems that are capable of performing work that one would describe as intelligent if a human did it. One of the "hottest" AI research areas is currently the neural network research. Applications of neural networks have been prevalent in control engineer … More ing.Advanced control systems for plant factories should include feedback and/or feed-forward loops with information obtained from growing plants using a set of various sensors. Realization of such a control system requires the development of a sensing system including a particular sensory information processing system for plant growth (Hashimoto and Nonami, 1992). The acquisition of multiple interactive information from growing plants in the plant factory poses significant challenges for the design of sensing systems. A possibility of applications of the finite element neural network to a plant factory control was reported in brief by Murase et al (1993).In this research the clear description of the finite element neural network that performs a non-linear mapping between a set of correlated input variables and correlated output variables of interest by a recurrent type neural network (Williams and Zipser, 1989) inspired by the finite element spatial representation is presented. As a practical example of the FENN applications, the FENN is utilized in order to establish a non-invasive determination method for the leaf water potentials of a greenhouse-grown chrysanthemum. The leaf water potential level of a plant is a useful index of plant water status. Currently no method is available for the non-invasive measurement of the leaf water potential of a plant.The conventional multilayred neural network is made up of many simple interconnected processing elements of which the connections are only mathematical. Artificial neural networks can be structured in a physical space. The finite element method can be employed in order to devise a spatial neural network. The individual units contained in artificial neural network can be interconnected physically by finite elements, serving as media that can conduct information or signals conceptually. The element nodes coincide with neurons so that a brain-like neural structure can be constructed in the Euclidian space. The all neurons of the FENN are connected to each other by media that can transmit information or signals just like a conventional recurrent neural network.This research showed that a neural network can be constructed in Euclidian space using finite elements. It was demonstrated that the FENN can serve as an artificial intelligence technique that can perform directing data processing. The FENN input cells that can serve as sensory units are all interactive dimensionally. From this study it can be concluded that the FENN has a great potential in engineering applications. Less

有限元神经网络（FENN）是一种无创监测温室大棚植物水分状况的技术。控制微分方程（泊松方程）用于神经信息处理。泊松方程的解决方案，得到了使用有限元技术。FENN的学习算法采用卡尔曼滤波器，作为FENN应用的一个实例，证明FENN提供了一种估算叶水势的方法（FENN的相关输出）的温室种植的菊花从其叶片的数字图像数据（FENN的相关输入）人工智能关注的是开发软件系统，这些软件系统能够执行如果人类使用智能，做到了。目前人工智能研究的“最热”领域之一是神经网络研究。神经网络在控制工程中的应用已经非常普遍 ...更多信息 植物工厂的先进控制系统应该包括反馈和/或前馈回路，该反馈和/或前馈回路具有使用一组各种传感器从生长的植物获得的信息。实现这样的控制系统需要开发一种传感系统，包括用于植物生长的特定传感信息处理系统（Hashimoto和Nonami，1992）。在植物工厂中，从生长的植物中获取多种交互式信息对传感系统的设计提出了重大挑战。Murase等（1993）曾简要地报道了有限元神经网络在工厂控制中应用的可能性，本文对有限元神经网络进行了清晰的描述，它通过一个递归型神经网络在一组相关的输入变量和相关的输出变量之间进行非线性映射（威廉姆斯和Zipser，1989）的方法。作为FENN应用的一个实例，利用FENN建立了温室菊花叶片水势的无创测定方法。植物叶水势水平是反映植物水分状况的一个有用指标。传统的多层神经网络是由许多简单的处理单元相互连接而成的，它们之间的连接只是数学上的。人工神经网络可以在物理空间中构建。可以采用有限元方法来设计空间神经网络。人工神经网络中包含的各个单元可以通过有限元在物理上互连，作为概念上传导信息或信号的介质。单元节点与神经元重合，从而可以在欧几里得空间中构造类脑神经结构。FENN的所有神经元都像传统的递归神经网络一样通过可以传输信息或信号的介质相互连接。这项研究表明，可以使用有限元在欧几里得空间中构建神经网络。事实证明，FENN可以作为一种可以执行直接数据处理的人工智能技术。作为感觉单位的FENN输入细胞在维度上都是相互作用的。从这项研究可以得出结论，FENN在工程应用中具有巨大的潜力。少

项目成果

Haruhiko Murase: "Finite Element Neural Network for Plant Water Status Non-invas Monitoring" Control Engineering Practice,J.of IFAC. (In Print). (1994)

Haruhiko Murase：“用于植物水状态非侵入监测的有限元神经网络”控制工程实践，J.of IFAC。

Haruhiko Murase: "The finite element neural network application to plant factory" Proc.of 12th World Congress International Federation of Automatic Control 10. 329-332 (1993)

Haruhiko Murase：“有限元神经网络在植物工厂中的应用”第十二届世界大会国际自动控制联合会 10. 329-332 (1993)

Haruhiko Murase: "The Finite Element Neural Network Application to Plant Factory" Proc.of 12th World Congress International Federation of Automatic Control. 10. 329-332 (1993)

Haruhiko Murase：“有限元神经网络在植物工厂中的应用”第十二届世界大会国际自动控制联合会会议记录。

Haruhiko Murase: "Finite Element Neural Network for Plant Water Status Non‐invas Monitoring" Control Engineering Practice,J.of IFAC. (In Print). (1994)

Haruhiko Murase：“用于植物水状态非侵入监测的有限元神经网络”《控制工程实践》，IFAC 杂志（印刷版）。

Haruhiko Murase: "Finite element neural network for plant water status non-invasive monitoring" Control Engineering Practice Journal of IFAC. (In Print). (1994)

Haruhiko Murase：《用于植物水分状态非侵入性监测的有限元神经网络》IFAC 控制工程实践杂志。