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자료유형
학술대회자료
저자정보
Inaba, S. (Laboratory of Agricultural Machinery, Faculty of Agriculture. Saga University) Takase, A. (Laboratory of Bioproduction Engineering, Department of Bioproduction Environment Science, Faculty of Agriculture, Kyushu Universit) Inoue, E. (Laboratory of Bioproduction Engineering, Department of Bioproduction Environment Science, Faculty of Agriculture, Kyushu Universit) Yada, K. (Laboratory of Bioproduction Engineering, Department of Bioproduction Environment Science, Faculty of Agriculture, Kyushu Universit) Hashiguchi, K. (Laboratory of Bioproduction Engineering, Department of Bioproduction Environment Science, Faculty of Agriculture, Kyushu University)
저널정보
한국농업기계학회 한국농업기계학회 International Conference 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
발행연도
2000.1
수록면
124 - 131 (8page)

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이 논문의 연구 히스토리 (2)

2000

ANALYTICAL SIMULATION OF TRAVEL RESISTANCE OF THE RUBBER CRAWLER SYSTEM FOR FARM MACHINERY
Inaba ,  S. ,  Inoue 외 5명 한국농업기계학회 International Conference 2000.01 학술대회자료
STUDY ON APPLICATION OF NEURO-COMPUTER TO NONLINEAR FACTORS FOR TRAVEL OF AGRICULTURAL CRAWLER VEHICLES
Inaba ,  S. ,  Takase 외 7명 한국농업기계학회 International Conference 2000.01 학술대회자료

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In this study, the NEURAL NETWORK (hereinafter referred to as NN) was applied to control of the nonlinear factors for turning movement of the crawler vehicle and experiment was carried out using a small model of crawler vehicle in order to inspect an application of NN. Furthermore, CHAOS NEURAL NETWORK (hereinafter referred to as CNN) was also applied to this control so as to compare with conventional NN. CNN is especially effective for plane in many variables with local minimum which conventional NN is apt to fall into, and it is relatively useful to nonlinear factors. Experiment of turning on the slope of crawler vehicle was performed in order to estimate an adaptability of nonlinear problems by NN and CNN. The inclination angles of the road surface which the vehicles travel on, were respectively 4deg, 8deg, 12deg. These field conditions were selected by the object for changing nonlinear magnitude in turning phenomenon of vehicle. Learning of NN and CNN was carried out by referring to positioning data obtained from measurement at every 15deg in turning. After learning, the sampling data at every 15deg were interpolated based on the constructed learning system of NN and CNN. Learning and simulation programs of NN and CNN were made by C language ("Association of research for algorithm of calculating machine (1992)"). As a result, conventional NN and CNN were available for interpolation of sampling data. Moreover, when nonlinear intensity is not so large under the field condition of small slope, interpolation performance of CNN was a little not so better than NN. However, when nonlinear intensity is large under the field condition of large slope, interpolation performance of CNN was relatively better than NN.
#Non Linear Factor #Neural Network #Chaos Neural Network #Agriculture Crawler Vehicle

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