scholarly journals A New Learning Algorithm for Function Approximation Incorporating A Priori Information into Extreme Learning Machine

2006 ◽  
pp. 631-636 ◽  
Author(s):  
Fei Han ◽  
Tat-Ming Lok ◽  
Michael R. Lyu
Keyword(s):  
Extreme Learning Machine ◽  
Function Approximation ◽  
Learning Algorithm ◽  
A Priori ◽  
A Priori Information ◽  
New Learning ◽  
Learning Machine ◽  
Priori Information

A Neural Root Finder of Polynomials Based on Root Moments

2004 ◽  
Vol 16 (8) ◽  
pp. 1721-1762 ◽  
Author(s):  
De-Shuang Huang ◽  
Horace H.S. Ip ◽  
Zheru Chi
Keyword(s):  
Learning Algorithm ◽  
A Priori ◽  
Feedforward Neural Networks ◽  
A Priori Information ◽  
Backpropagation Algorithm ◽  
Constrained Learning ◽  
Roots Of Polynomials ◽  
Simulation Results ◽  
Priori Information ◽  
Coefficient Method

This letter proposes a novel neural root finder based on the root moment method (RMM) to find the arbitrary roots (including complex ones) of arbitrary polynomials. This neural root finder (NRF) was designed based on feedforward neural networks (FNN) and trained with a constrained learning algorithm (CLA). Specifically, we have incorporated the a priori information about the root moments of polynomials into the conventional backpropagation algorithm (BPA), to construct a new CLA. The resulting NRF is shown to be able to rapidly estimate the distributions of roots of polynomials. We study and compare the advantage of the RMM-based NRF over the previous root coefficient method—based NRF and the traditional Muller and Laguerre methods as well as the mathematica roots function, and the behaviors, the accuracies of the resulting root finders, and their training speeds of two specific structures corresponding to this FNN root finder: the log σand the σ FNN. We also analyze the effects of the three controlling parameters {δP0 θp η} with the CLA on the two NRFs theoretically and experimentally. Finally, we present computer simulation results to support our claims.

MISEP Method for Postnonlinear Blind Source Separation

2007 ◽  
Vol 19 (9) ◽  
pp. 2557-2578 ◽  
Author(s):  
Chun-Hou Zheng ◽  
De-Shuang Huang ◽  
Kang Li ◽  
George Irwin ◽  
Zhan-Li Sun
Keyword(s):  
Blind Source Separation ◽  
Learning Algorithm ◽  
A Priori ◽  
Source Separation ◽  
A Priori Information ◽  
Separation Algorithm ◽  
Linear Network ◽  
Output Entropy ◽  
Effectiveness And Efficiency ◽  
Priori Information

In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network are used as the unmixing system to separate sources in the postnonlinear mixtures, and another group of three-layered perceptron is used as the auxiliary network. The learning algorithm for the unmixing system is then obtained by maximizing the output entropy of the auxiliary network. The proposed method is applied to postnonlinear blind source separation of both simulation signals and real speech signals, and the experimental results demonstrate its effectiveness and efficiency in comparison with existing methods.

Lithofacies Recognition Based on Extreme Learning Machine

2012 ◽  
Vol 241-244 ◽  
pp. 1762-1767 ◽  
Author(s):  
Ya Juan Tian ◽  
Hua Xian Pan ◽  
Xuan Chao Liu ◽  
Guo Jian Cheng
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Selection Process ◽  
Feedforward Neural Networks ◽  
Parameter Selection ◽  
Classification Model ◽  
Support Vector ◽  
New Learning ◽  
Learning Machine ◽  
Hidden Layer

To overcome the problem of lower training speed and difficulty parameter selection in traditional support vector machine (SVM), a method based on extreme learning machine (ELM) for lithofacies recognition is presented in this paper. ELM is a new learning algorithm with single-hidden layer feedforward neural networks (SLFNN). Not only it can simplify the parameter selection process, but also improve the training speed of the network learning. By determining the optimal parameters, the lithofacies classification model is established, and the classification result of ELM is also compared to traditional SVM. The experimental results show that, ELM with less number of neurons has similar classification accuracy compared to SVM, and it is easier to select the parameters which significantly reduce the training speed. The feasibility of ELM for lithofacies recognition and the availability of the algorithm are verified and validated

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