Trainable Finite Element Neural Network and Intelligent Imaging Device for Pattern Recognition of Biological Object
Project/Area Number  04660269 
Research Category 
GrantinAid for Scientific Research (C).

Research Field 
農業機械

Research Institution  University of Osaka Prefecture 
Principal Investigator 
MURASE Haruhiko Univ.of Osaka Pref., Agri.Eng.Dept., Asoc.Prof., 農学部, 助教授 (20137243)

CoInvestigator(Kenkyūbuntansha) 
NISHIURA Yoshifumi Univ.of Osaka Pref., Agri.Eng.Dept., Asoc.Prof., 農学部, 助手 (80221472)
TAKIGAWA Hiroshi Univ.of Osaka Pref., Agri.Eng.Dept., Asoc.Prof., 農学部, 助手 (30081566)
HONAMI Nobuo Univ.of Osaka Pref., Agri.Eng.Dept., Asoc.Prof., 農学部, 教授 (50081493)

Project Fiscal Year 
1992 – 1993

Project Status 
Completed(Fiscal Year 1993)

Budget Amount *help 
¥2,100,000 (Direct Cost : ¥2,100,000)
Fiscal Year 1993 : ¥300,000 (Direct Cost : ¥300,000)
Fiscal Year 1992 : ¥1,800,000 (Direct Cost : ¥1,800,000)

Keywords  FINITE ELEMENT NEURAL NETWORK / PATTERN RECOGNITION / FUNCTIONAL DEVICE / NEURAL NET / IMAGING DEVICE / 有限要素神経回路網 / パターン認識 / 機能性素子 / ニューラルネット / 撮像デバイス 
Research Abstract 
The finite element neural network (FENN) was appied to a non invasive technique to monitor the plant water status of greenhousegrown 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 greenhousegrown 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
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ing. Advanced control systems for plant factories should include feedback and/or feedforward 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 nonlinear 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 noninvasive determination method for the leaf water potentials of a greenhousegrown chrysanthemum. The leaf water potential level of a plant is a useful index of plant water status. Currently no method is available for the noninvasive 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 brainlike 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

Report
(4results)
Research Output
(8results)