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：
目前常用的物体识别方法，其过程非常复杂，信息量和计算量都很大。结合改进遗传算法的神经网络方法，采用将结构与误差结合的适应度函数，改进的遗传算子实现对BP网络结构和权值的同步优化：提出一种用改进遗传算法优化后的BP神经网络进行物体识别，并以提取的修正不变矩特征作为BP神经网络的输入，仿真结果表明该方法提高了识别的稳定性和收敛性能，并且识别率较高：从而验证了该方法的有效性：
英文摘要：
Algorithms for recognition are cnmplcx,and the information and computation are large at present.Neural network based on improved genetic algorithm adopts fitness function of combining structure and error and the improved genetic operator to implement the optimization of structure and weights of BP network simultaneity.To recognize objections,BP neural network based on improved genetic algorithm is proposed in the paper,and the improved invariant moments extracted are regarded as the imput of BP network.The simulation results indicate that the method improves the stability and convergence capability of recognition.Moreover,the recognition rate is very high.So the efficiency is proved in the paper.
同期刊论文项目
同项目期刊论文
期刊信息
位置：