【摘要】 提出了一种基于免疫识别原理的径向基函数神经网络学习算法.该算法利用人工免疫系统的识别、记忆、学习等原理,将输入数据作为抗原,抗体为抗原的压缩映射作为径向基函数神经网络模型的隐层中心,输出采用最小二乘法确定权值.通过预报热轧带肋钢筋力学性能的仿真实验结果表明,与K-均值法选择中心点比较,该算法计算量较小,精度高.更多还原

【Abstract】 A Radial Basis Function (RBF) neural network learning algorithm based on immune recognition principle is proposed. In the algorithm, the input data are regarded as antigens and the compression mappings of antigens as antibodies, i.e., the hidden layer centers. This algorithm can choose the number and location of the hidden layer centers by applying the principles of recognition, memory and learning, and can determine the weights of the output layer by adopting the least square algorithm. The predicted results of the mechanical property of hot-rolled steel bars show that this algorithm has the advantages of less computation and high precision compared to the K-means algorithm.更多还原