The extreme learning machine (ELM), a new learning algorithm for single hidden layer feedforward neural networks (SLFN), has drawn interest of a large number of researchers, especially due to its training speed and good generalization performances compared to known machine learning methods. The ELM model generates a solution to a linear optimization problem for the hidden layer output weights by randomly generating input weights and biases, instead of iteratively adjusting the network parameters (weights and biases) such as the backpropagation neural network model using the backpropagation algorithm and gradient descent learning. However, random determination of input weights and hidden layer bias can result in non-optimal parameters that have an adverse impact on the final results or require a higher number of hidden nodes for the neural network. In this study, a new hybrid method is proposed to overcome the drawbacks caused by non-optimal input weights and hidden biases. This hybrid method, which is called CPN-ELM algorithm, uses the counter propagation network (CPN) model to systematically optimize input weights, hidden layer neurons and hidden biases. The performance of CPN-ELM compared to traditional ELM method was examined on three benchmark regression datasets and we observed that the model produced higher accuracy values for each datasets.

极限学习机（ELM）是一种新的单隐层前馈神经网络（SLFN）学习算法，特别是由于其训练速度快，泛化性能优于已知的机器学习方法，引起了众多研究人员的兴趣。ELM模型通过随机生成输入权重和偏差来生成隐藏层输出权重的线性优化问题的解决方案，而不是像使用反向传播算法和梯度的反向传播神经网络模型那样迭代调整网络参数（权重和偏差）下降学习。然而，输入权重和隐藏层偏差的随机确定可能会导致非最优参数，从而对最终结果产生不利影响，或者需要更多数量的神经网络隐藏节点。在这项研究中，提出了一种新的混合方法来克服非最优输入权重和隐藏偏差造成的缺点。这种称为 CPN-ELM 算法的混合方法使用反向传播网络 (CPN) 模型来系统地优化输入权重、隐藏层神经元和隐藏偏差。与传统 ELM 方法相比，CPN-ELM 的性能在三个基准回归数据集上进行了检查，我们观察到该模型为每个数据集产生了更高的准确度值。