针对海洋矿物分类问题,提出了改进后的单输出切比雪夫多项式神经网络(single-output Chebyshev-polynomial neural network with general solution,SOCPNN-G).该模型利用伪逆的通解来求参数,扩大解空间,能获得泛化性能更加优良的权重.在该模型中,子集方法用于确定神经元的初始数量和获得交叉验证的最佳重数.最后将改进的SOCPNN-G模型用于海洋矿物数据集中进行实验,结果表明,该模型训练准确率和测试准确率分别达到90.96%和83.33%,且对计算性能要求较低.这些优越性表明该模型在海洋矿物的实际应用中具有很好的前景.
Aiming at the classification of marine minerals, an improved single-output Chebyshev-polynomialneural network with general solution ( SOCPNN-G) was proposed. This model uses the general solution of pseudo-inverse to find the parameters and expand the solution space, and it can obtain weights with better ge-neralization performances. In addition, in this model, the subset method was used to determine the initial nu-mber of neurons and obtain the optimal number of the cross validation. Finally, the modified SOCPNN-G was tested in the marine mineral data set. The experimental results show that the training accuracy and test accura-cy of the model can reach 90. 96% and 83. 33%, respectively, and the requirements for computing perfor-mance are low. These advantages indicate that this model has excellent application prospects in marine minerals.