改进粒子群算法在治疗型关节炎护膝中的应用

为保证治疗型关节炎护膝中升压电路的优良性能，必须对其中的电路参数进行优化。本文采用一种改进的粒子群算法对升压电路中控制强度的参数进行优化，保证平稳的升压性能。同时为了保证粒子群在搜索过程中目标函数的可计算，采用神经网络对升压电路进行建模，通过实验数据对神经网络进行训练并测试。最后分别在粒子群优化后的参数与原有参数下，对升压电路性能进行比较，验证了该算法所确定的参数可以保证升压过程更平稳，电路具有更好的性能。

Application of improved particle swarm optimization to the kneepad used for curing osteoarthritis

In order to insure good performance of the voltage boosting circuit in the kneepad used for curing osteoarthritis, the circuit parameters should be optimized. An improved particle swarm optimization algorithm is used to optimize the circuit parameters that control the output strength. At the same time, in order to insure the calculability of the objective function in the searching process of the particle swarm, neural network is used to model the voltage boosting circuit. The neural network is trained and tested using the experimental data. Finally the performances of the voltage boosting circuits with both optimized parameters and original parameters are tested and compared respectively. The test result proves that the voltage boosting circuit with optimized parameters using the proposed algorithm can insure smoother voltage boosting, and possesses better performance.