欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为解决离散过程神经网络的训练问题，提出了两种基于数值积分的离散过程神经网络训练算法．分别采用三次样条积分和抛物插值积分直接处理隐层离散样本和权值的时域聚合运算，输出层采用普通神经元，采用L—M（Levenberg—Marquard）算法实现网络参数的调整．以模糊图像的恢复为例，实验结果表明，两种训练方法的性能比较接近，但都优于基于沃尔什变换的离散过程神经网络和基于样条差值函数的离散过程神经网络，从而揭示出数值积分方法在提升离散过程神经网络性能和应用方面具有一定潜力。
英文摘要：
To address the training problem of discrete process neural networks, two training algorithms based on numeri- cal integration were proposed. The cubic spline integration and the parabolic interpolation integration were used in the hid- den layer to deal with the time-domain aggregation of discrete samples and weights. The classical neurons were used in out- put layer. In order to improve the convergence ability of the network, the Levenberg-Marquard algorithm was employed to ad- just the networks＇ parameters. The effeeiveness of the proposed algorithms was testified by applying the network to the resto- ration of a fuzzy image. Experimental results show that the performance of the two algorithms is relatively close and is superior to the Walsh transformation-based discrete process neural networks and spline function-based diserete process neural networks in both approximation ability and effect of image restoration, which reveals that the proposed methods of numerical integration have some potential in the performance improvement and application extension of discrete process neural networks.
同期刊论文项目
同项目期刊论文
Quantum ant colony optimization algorithm based on Bloch spherical search (SCI: 000309320500002; EI:
Model and algorithm of quantum inspired neural network with sequence input based on controlled rotat
期刊信息
位置：