研究过程神经网络的学习算法及其在过程模式识别中的应用。针对权值基展开的过程神经网络讨论了权值基的选取规则和对采样曲线的标准化处理问题，给出了含一个隐层的过程神经网络的误差反传播学习算法。以聚合化学反应和渗流实验两个具体实例验证了算法的有效性，也说明了过程神经网络的广泛应用前景。

This paper deals with learning algorithms for procedure neural networks (PNN) and its applications in aggregation chemical reaction and seepage test in oil geology. Weight bases selection rules and pattern curve standard problems are also discussed. These examples show that PNN have extensive applications.