Rationale NMDA-R (N-methyl-D-aspartate receptors) havebeen implicated in synaptic plasticity underlying one-triallearning of event-place associations. In rodents, episodic-like memory (ELM) of personally experienced events canbe inferred from behavior that reflects the remembrance ofthe content (what kind of object was presented), place(where was this object placed),...

We focus on JET plasmas in which ELMs are triggered by pellets in the presence of ELMs which occur naturally. We perform direct time domain analysis of signals from fast radial field coils and toroidal full flux azimuthal loops. These toroidally integrating signals provide simultaneous high time resolution measurements of global plasma dynamics and its coupling to the control system. We examine...

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...

This paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM). AF is the most common irregular heart beat disease which may cause many cardiac diseases as well. Bispectral analysis was used to extract the nonlinear information in the ECG signals. The bispectral features of each ECG episode were d...

Extreme learning machine (ELM) is one of the most popular and important learning algorithms. It comes from single-hidden layer feedforward neural networks. It has been proved that ELM can achieve better performance than support vector machine(SVM) in regression and classification. In this paper, mathematically, with regression problem, the step 3 of ELM is studied. First of all, the equation Hβ...

Feedforward neural networks (FFNN) have been utilised for various research in machine learning and they have gained a significantly wide acceptance. However, it was recently noted that the feedforward neural network has been functioning slower than needed. As a result, it has created critical bottlenecks among its applications. Extreme Learning Machines (ELM) were suggested as alternative learn...

Crowd density estimation in public areas with people gathering and waiting is the important content of intelligent crowd surveillance. A real-time and high accuracy algorithm is necessary to be inputted in the classification and regression of crowd density estimation to improve the speed and increase the efficiency. Extreme Learning Machine (ELM) is a neural network architecture in which hidden...

We present an experimental and theoretical study of the breakthrough performance of the flexible metal-organic framework Cu(bpy)2(BF4)2 (bpy = 4,4'-bipyridine), termed ELM-11. Pure CO2, He, CH4, and N2 gases, as well as binary gas mixtures of those species, were used to perform breakthrough experiments on ELM-11. ELM-11 exhibits a stepped breakthrough curve for CO2 not seen in rigid adsorbents....

—Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers. However, its good generalization ability is built on large numbers of hidden neurons, which is not beneficial to real time response in the test process. In this paper, we proposed new ways, named " constrained extreme lear...

Accurate and effective stock price prediction is appealing for investors due to the potential of obtaining a very high return. However, it is still a challenging task in the modern business world because of the complex, evolutionary, and nonlinear nature of stock market. Therefore, we proposed two hybrid models, which are Harmony Search (HS) based Extreme Learning Machine (ELM) that is denoted ...