A Model of Gaussian Radial Basis Function Networks Based on Evolutionary Computation
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摘要: 为了提高高斯径向基神经网络模型的构建精度,将径向基中心、基宽以及连接权构成分区实数编码结构,将训练样本集作为每一个进化个体解码后的网络输入及输出,并将样本的期望输出同网络实际输出的平均误差平方和作为进化个体的适应度函数,将不同隐层节点数构成的进化个体的最优值作为设计问题的高斯径向基网络结构.采用2个Benchmark测试函数验证在不同隐层节点数情况下通过该进化算法构建的径向基模型的精度,从进化时间、进化代、最小适应度值以及均方根误差等方面作对比.结果表明,采用这种分区实数编码能高精度地构建不同设计问题的高斯径向基网络模型Abstract: To improve the construction accuracy of Gaussian radial basis neural network model, a partition real number coding method was proposed by using Gaussian radial basis centers, width, and the connection weights. In this method, the training sample set was used as the network input and output after decoding each individual, and the sum of mean squared error between expectation output of the samples and actual network output was used as evolutionary individual fitness function, and the optimal value of the evolutionary individuals which consist of different hidden nodes was used as the final Gaussian RBF network structure. Simulations were carried out to test the proposed method through two benchmark testing functions. The accuracy of the proposed model construction method was evaluated in terms of different hidden nodes, evolutionary time, number of evolutiongeneration, minimal fitness and the root mean square error. Simulation results showed that the proposed model construction method has high accuracy for different design problems.
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