首页|Reducer lubrication optimization with an optimization spiking neural P system
Reducer lubrication optimization with an optimization spiking neural P system
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NSTL
Elsevier
It is very difficult to improve the efficiency and accuracy of reducer lubrication due to the limitation of the traditional simulation method. In this paper, an optimization model with multiple parameters is first established to reflect the relationship between the churning loss and the optimized parameters of the zero-backlash high-precision roller developing reducer (ZHPRER). Then, an optimization spiking neural P system (OSNPS) is applied to optimize the multiparameter model. Finally, a simulation analysis (the semi-implicit moving particle method, MPS) is used to verify the correctness of the optimization results. The experimental results show that the multiparameter optimization model and OSNPS are effective and accurate for solving the multiparameter optimization problem of ZHPRER by MPS method.
NSTL
Elsevier
