针对峰值法中测量路径规划问题，提出了基于参数模型学习的神经网络预测器。
Aiming at measuring path planning by using peak value, neural network predictor based on parameter model is presented.
仿真结果表明：该P SO优化SVR参数方法可行、有效，由此得到的SVR模型具有更好的学习精度和推广能力。
Simulation results show that the optimal selection approach based on PSO is available and the PSO-SVR model has superior learning accuracy and generalization performance.
理论分析说明这种模糊规则后件参数学习算法是收敛的、所建模糊模型能够以要求的精度逼近已知的实验数据。
The learning algorithm and the characteristics of the fuzzy rules model which can approximate the experiment data are shown to converge to any arbitrary accuracy by the theoretical analysis.
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