Abstract: The powertrain efficiency of the engineering vehicle under the heavy load is decreased obviously. In order to solve this problem, the method of parallel self-adaptive neural network was employed based on “two parameters” shift schedule, which of structure include neural network control, self-adaptive neural network model, network evaluation and running monitor model. The simulation results showed that the intelligent shift control could improve the powertrain efficiency of the engineering vehicle, and could overcome the low realtime behavior of neural network in the meantime. |
Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.
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