Extreme learning machine (ELM) has emerged as an efficient and effective learning algorithm for classification and regression tasks. Most of the existing research on the ELMs mainly focus on supervised learning. Recently, researchers have extended ELMs for semi-supervised learning, in which they exploit both the labeled and unlabeled data in order to enhance the learning performances. They have...

Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with a high fuel efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates a model based control approach for automotive application. HCCI engine control is a nonlinear, multi-input multi-output problem with state and actuator constraints w...

This paper proposes the online sequential extreme learning machine algorithm based on the recursive partial leastsquares method (OS-ELM-RPLS). It is an improvement to the online sequential extreme learning machine based on recursive least-squares (OS-ELM-RLS) introduced in [1]. Like in the batch extreme learning machine (ELM), in OSELM-RLS the input weights of a single-hidden layer feedforward ...

Time-Varying Neural Networks(TV-NN) represent a powerful tool for nonstationary systems identification tasks, as shown in some recent works of the authors. Extreme Learning Machine approach can train TV-NNs efficiently: the reference algorithm is named ELM-TV and is of batch-learning type. In this paper, we generalize an online sequential version of ELM to TV-NN and evaluate its performances in...

A new image denoising algorithm is proposed. GA-ELM algorithm uses genetic algorithm (GA) to decide weights in the Extreme learning Machine algorithm. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-ELM to do image denoising researching work. Firstly, this paper uses training samples to train GA-ELM as the noise detector. Then, we utili...

This paper presents an online learning with regularized kernel based one-class extreme learning machine (ELM) classifier and is referred as “online RK-OC-ELM”. The baseline kernel hyperplane model considers whole data in a single chunk with regularized ELM approach for offline learning in case of one-class classification (OCC). Further, the basic hyper plane model is adapted in an online fashio...

Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated; the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learn...

In this study, gene expression profile classification is done via sparse representation in the random feature Space, which is obtained by either random projection or nonlinear random mapping used in Extreme learning machine (ELM). The numerical experiment shows that sparse representation has slightly better performance than ELM.

Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on physically locating specific landmarks, it is a boring, lengthy and error prone task. The objective of this work is to calculate the SNA angle, SNB angle and ANB angle between the landmarks to identify the input and output parameters pertaining to skeletal abnormalities. By doing s...