猫群RBF神经网络诊断方法研究

Research of Diagnosis Method Based on Cat Swarm RBF Neural Network

神经网络的参数设置直接影响其诊断正确率。以猫群算法优化径向基函数神经网络参数,建立最优的RBF神经网络诊断模型,利用建立的诊断模型进行肺癌诊断实验分析。结果表明:所建猫群RBF神经网络诊断模型有较高的诊断正确率,克服了RBF神经网络参数确定的盲目性,对肺癌患者判断的准确率要高于一般的BP神经网络。

Parameter setting for neural network directly affects the diagnosis performance.In this paper,RBF neural network parameters were optimized by cat swarm algorithm.The optimal RBF neural network model was established.It was used to diagnose early lung cancer and carry out experiment analysis.The test results showed that the presented model had higher diagnosis accuracy,overcoming the blindness of parameter setting for RBF.It was able to judge the patients with lung cancer more accurately and better than BP neural network diagnosis model.