式中: βj为第j 个隐含层节点和输出节点之间的权重值，wj为第j 个

Where: βj is the weight value between the jth hidden layer node and the output node, wj is the weight value between the jth hidden layer node and the input node, and bj <br>is the bias of the jth hidden layer node Set. The ELM algorithm randomly selects the input weights and biases of the hidden layer nodes, and calculates the weight values ​​from the hidden layer node to the output node based on the principle of least squares, so the learning speed of the algorithm is very fast.

In the formula: betaj is the weight value between the j implied layer node and the output node, wj is the weight value between the j implied layer node and the input node, bj<br>Bias for the j-hidden layer node. The ELM algorithm randomly selects the input weight and bias of the implied layer node, and calculates the weight value of the output implied layer node to the output node based on the principle of least squares, so the learning speed of the algorithm is very fast.

Where: β J is the weight value between the jth hidden layer node and the output node, WJ is the weight value between the jth hidden layer node and the input node, bj<br>Is the offset of the j th hidden layer node. The elm algorithm randomly selects the input weights and offsets of the hidden layer nodes, and calculates the weights from the output hidden layer nodes to the output nodes based on the principle of the least square method, so the learning speed of the algorithm is very fast.