针对间歇过程中最优操作轨线经常从生产经验中人工获取的问题,提出了一种基于过程神经网络(processneural network,PNN)模型的自动计算方法。利用PNN独特的时间 .
基于2个网页-相关网页
自组织过程神经网络 SOPNN
反馈过程神经网络 Feedback process neural network ; feedback procedure neural networks
过程神经元网络 process neural network
自组织过程神经元网络 FPNN
反馈过程神经元网络 feedback process neural networks
多聚合过程神经元网络 multi-aggregation process neural networks
提出一种基于过程神经网络的木材生长轮密度长期预测方法。
A long-term forecast method of timber growth ring density based on process neural network was proposed in this paper.
应用表明，算法简化了过程神经网络的计算复杂度，提高了网络学习效率和对实际问题求解的适应性。
The application shows that the algorithms simplify the computing complexity of process neural networks, and raise the efficiency of the network learning and the adaptability to real problem resolving.
仿真结果表明，该系统响应快，无超调，比传统的加工过程神经网络自适应控制具有更好的控制效果。
Simulation results show that the designed system is of fast response, non-overshoot and it is more effective than the conventional adaptive control of machining process based on neural network.
应用推荐