| Project/Area Number |
03660269
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
農業気象・生物環境制御学
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| Research Institution | Ehime University |
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Principal Investigator |
MORIMOTO Tetsuo Ehime University, Dept.of Biomechanical Systems, Assistant, 農学部, 助手 (50127916)
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| Co-Investigator(Kenkyū-buntansha) |
FUKUYAMA Toshio Ehime University, Dept.of Biomechanical Systems, Assistant professor, 農学部, 助教授 (90036351)
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| Project Period (FY) |
1991 – 1993
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| Project Status |
Completed (Fiscal Year 1993)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1993: ¥200,000 (Direct Cost: ¥200,000)
Fiscal Year 1992: ¥100,000 (Direct Cost: ¥100,000)
Fiscal Year 1991: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Fuzzy logic / Fuzziness / Neural networks / Modeling by learning / Hydroponic tomato / Calcium deficiency / Hydroponic control / Ion composition / ニューラルネット / ニュ-ラルネットワ-ク |
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| Research Abstract |
There are many physiological disorders, e.g.blossom-end rot of fruits, caused by calcium deficiency in a hydroponic tomato cultivation. In order to obtain better quality of fruits in a tomato cultivation, effective control of the hydroponic system is neccessay. However, the control system is characterized by complexity and fuzziness. In this study, intelligent control techniques based on fuzzy logic, neural networks and genetic algorithm are developed and then applied to the optimal control of hydroponic tomato cultivation.
(1) In the tomato cultivation, marked calcium ion uptake was observed during the reproductive growth. So, the calcium deficiency in tomato fruits seems to be caused by rather low calcium ion transportaion to the fruits than low calcium ion uptake.
(2) A newly developed adaptive control technique combining with the feedback control based on fuzzy logic and the feedforward control based on neural networks was effective for the time-varying control problem of the hydroponic nutrient solution. Applying the fuzzy logic increased flexibility and smoothness of the control performance. Furthermore, applying the inverse system model based on neural networks, more speedy and adaptive control performances were obtained.
(3) A newly developed optimal control technique combining with neural networks and genetic algorithms was useful for optimization of net photosynthetic rate of a plant. In the method, the net photosynthetic rate to intermittent solution supply is at first identified by using neural networks and then optimal values of 4-step drainage and supply times for intermittent solution supply are determined through the model simulation by using genetic algorithms. The use of neural networks made it possible to identify such complex systems as net photosynthetic rate. Furthermore, the genetic algorithms enable us to quickly search the optimal values.
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