中文摘要：

针对传统优化方法提高径向基函数神经网络（RBFNN）分类能力存在的问题，提出一种基于合作型协同进化群体并行搜索的CO—RBFNN学习算法．该算法首先利用K-均值算法对最近邻方法确定的网络初始隐节点聚类，然后以聚类后的隐节点群作为子种群进行协同进化操作，最终获得网络的最优结构．算法采用包含整个网络隐节点结构和控制向量的矩阵式混合编码方式，隐层和输出层之间的连接权值由伪逆法确定．在UCI的8个数据集上进行的仿真实验结果验证该算法的有效性和可行性．

英文摘要：

A new algorithm is presented to improve the classification ability of the radial basis function neural network （RBFNN）. It attempts to construct RBFNN based on a cooperative coevolutionary algorithm. The K -means method is employed and the initial hidden nodes are divided into modules to represent the species of the coevolutionary algorithms. The good individuals in all species are found and then combined to form the whole structure of RBFNN. A matrix-form mixed encoding scheme with a control vector is adopted in this algorithm. The weights between the hidden layer and the output layer are calculated by pseudo- inverse algorithm. The proposed algorithm is tested on UCI datasets and the results show it outperforms the other existing methods with higher accuracy and simpler network construction.