The selection of starting center points of clustering has great effects on the constringency speed of this clustering algorithms and the performance of clustering.
聚类初始中心的选择对该聚类算法的收敛速度和聚类的性能都有很大的影响。
The constringency speed and generalization ability of optimized BPNN model are better than that of simple BPNN model, and the simulation result is close to reality.
遗传算法优化的BP神经网络在收敛速度和泛化能力上都较简单的BP神经网络要好，模拟结果更接近于真实值。
The result of simulation indicates that not only the algorithm has quick constringency speed and better stability, but also the result of optimizing is more satisfactory.
与其他免疫优化算法的对比仿真结果表明其不但具有较快的收敛速度和较好的稳定性，而且优化效果更为令人满意。
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