## 1 Introduction
A two-stage neural network using an Elman recurrent neural network and BP neural network was developed by [PERSON] [11] to predict typical values of fault feature variables and fault types. To anticipate short-term wind power, [PERSON] [12] developed a prediction model based on a convolutional neural network (CNN) and genetic algorithm (GA) wind power prediction model for the development of wind power generation and the research status of wind power prediction technology. Different prediction methods such as logistic regression, rule-based method, simple Bayesian classifier, artificial neural network method, k-nearest neighbor method [13], decision tree, support vector machine [14], and extreme learning machine (ELM) [15; 16; 17; 18] can be used. [PERSON] [15] found out that for small- to medium-sized training data, the user can select to utilize a method based on the training data's size (complexity of calculation is dependent on the amount of training data) or hidden nodes' size (complexity of computation is based on the number of hidden nodes). The dimensionality of the mapped space is determined by the number of hidden nodes. In general, the more sophisticated the training examplesare, the more hidden nodes are needed to train a generalized model to estimate the target functions for a large data issue. When dealing with highly big and complicated training datasets, this causes challenges for ELM. [PERSON] [16] found that due to the enormous number of hidden nodes required, the ELM network is quite massive, and the computing process becomes very expensive. [PERSON] [17] discussed the ELM method, which can be utilized to directly train neural networks using threshold functions. Many academics have lately researched extreme learning machines (ELM) as a sort of generalized single-hidden-layer feed-forward network in theory and applications. ELM's hidden node parameters (input weights and hidden layer biases) are generated at random, and the output weights are then computed analytically using the generalized inverse approach. An ELM-based equality-constrained optimization approach with two solutions for different training data sizes was illustrated. However, out of all these methods, [PERSON] [19] proposed ELM. It is more efficient as ELM has a swift learning rate. ELM was applied to find out the correlation between operational parameters and nitrogen oxide emissions of the boiler. Results illustrated that the ELM model was shown to be more exact and quicker than the common artificial neural network and support vector regression models in modeling nitrogen oxide emissions. In [20], the power flow, active power, and reactive power models of electric springs to regulate voltage were replaced by three data-driven models based on the extreme learning machine (ELM). To develop the final control strategies, an ELM-based control model was provided. In [21], an extreme learning machine (ELM) artificial neural network and four eddy current sensors were used to develop a measuring technique for the rotation angle of the spherical joint. The developed prototype showed measurement accuracy as well as offered a high-precision measuring approach. Through the determination of the picture coordinates of ripe fruits and fruit trees, the extreme learning machine algorithm was incorporated into the agricultural equipment navigation system, together with the BP neural network algorithm in [22], to accomplish the speedy navigation of the operation of agricultural machinery. The test findings reveal that employing the extreme learning machine, which can match the design criteria of contemporary agricultural machinery and equipment, has enhanced picking efficiency and accuracy significantly. The authors addressed accurate Maximum Power Point Tracking (MPPT) for PV-based Distributed Generation (DG) in [23] using an error-optimized (via Improved Water Cycle) Extreme Learning Machine with Ridge Regression (IWC-ELMRR).