Mine geological disaster is a complex non-linear system. The traditional prediction model has the disadvantages of low accuracy and poor reliability. In order to solve this problem, open-pit mine slope displacement taken as research object. Based on new algorithm extreme learning machine (ELM), intelligent sparrow search (SSA) are introduced determine weights thresholds input layer hidden ELM. ...

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is ...

the aim of the present paper is to evaluate the influence of the soil- foundation- structure interaction (sfsi) effects on the component demand modifier factor of concrete gravity beams based on asce 41-06 standard. to this end, the beam on the nonlinear winkler foundation approach is employed which is a simple and efficient method. at first, four sets of 3-, 6-, 10- and 15-storey concrete mome...

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a me...

In this paper, we propose an image classification method for improving the learning speed of convolutional neural networks (CNN). Although CNN is widely used in multiclass image classification datasets, the learning speed remains slow for large amounts of data. Therefore, we attempted to improve the learning speed by applying an extreme learning machine (ELM). We propose a learning method combi...

This paper investigates distributed cooperative learning algorithms for data processing in a network setting. Specifically, the extreme learning machine (ELM) is introduced to train a set of data distributed across several components, and each component runs a program on a subset of the entire data. In this scheme, there is no requirement for a fusion center in the network due to e.g., practica...

This paper deals with simulation studies of the adaptive control on the continuous stirred tank reactor (CSTR) as a typical chemical equipment with nonlinear behaviour and continuously distributed parameters. Mathematical model of this reactor is described by the set of two nonlinear ordinary differential equations (ODE). The simulation of the steady-state and dynamics results in optimal workin...

The extreme learning machine (ELM) is a newly emerging supervised learning method. In order to use the information provided by unlabeled samples and improve the performance of the ELM, we deformed the kernel in the ELM by modeling the marginal distribution with the graph Laplacian, which is built with both labeled and unlabeled samples. We further approximated the deformed kernel by means of ra...

In this paper, we present one dynamic model hypothesis to perform fish trajectory tracking in the fish ethology research and develop the relevant mathematical criterion on the basis of the Extreme Learning Machine (ELM). It is shown that the proposed scheme can conduct the non-linear and non Gaussian tracking process by multiple historical cues and current predictions – the state vector motion,...

We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph-cut optimization constrained by the super-face boundaries obt...