BA_EM_ELM.m文件是用于实现一个基于EM算法的ELM模型的程序。

简介：
This function, designated as BA_EM_ELM, calculates and returns several key performance metrics during a Bayesian-regularized Expectation-Maximization (EM) algorithm applied to an Extreme Learning Machine (ELM) model. The primary purpose is to train an ELM using a training dataset and subsequently evaluate its performance on a separate testing dataset. The function’s core functionality involves loading the provided training data, denoted as `train_data`, and processing it to extract relevant features. Specifically, the training datas output values are assigned to the variable `T`, representing a vector of length N. Furthermore, the function incorporates global variables – `I`, `T`, `I_test`, `T_test`, `c`, `d`, `h0` – which are utilized throughout the EM algorithms execution. The algorithm iteratively refines model parameters through the Expectation and Maximization steps. The function ultimately yields several outputs: `TrainingTime` and `TestingTime` representing the computational durations for each stage; `TrainingAccuracy` and `TestingAccuracy` indicating the respective accuracy scores achieved by the trained model; `TY_test` holding the test results; `num` storing an intermediate numerical value; and finally, `L`, `R`, `Q`, `F`, and `TY` representing further calculated values within the EM process. The function also utilizes parameters defined as input: the maximum value for regularization (`Lmax`) and a delta value (`delta`).