基于单输出切比雪夫多项式神经网络的海洋矿物分类

Marine Mineral Classification Based on Single-Output Chebyshev-Polynomial Neural Network

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-polynomial neural 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 accuracy 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.