A New Learning Algorithm for Function Approximation By Incorporating A Priori Information Into Feedforward Neural Networks

2007 ◽  
Author(s):  
Fei Han ◽  
Qing-Hua Ling
Keyword(s):  
Neural Networks ◽  
Function Approximation ◽  
Learning Algorithm ◽  
A Priori ◽  
Feedforward Neural Networks ◽  
A Priori Information ◽  
New Learning ◽  
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.

NEW LEARNING AUTOMATA BASED ALGORITHMS FOR ADAPTATION OF BACKPROPAGATION ALGORITHM PARAMETERS

2002 ◽  
Vol 12 (01) ◽  
pp. 45-67 ◽  
Author(s):  
M. R. MEYBODI ◽  
H. BEIGY
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Learning Automata ◽  
Feedforward Neural Networks ◽  
Bp Algorithm ◽  
Backpropagation Algorithm ◽  
New Learning ◽  
The Neural Networks ◽  
Factor Α ◽  
High Possibility

One popular learning algorithm for feedforward neural networks is the backpropagation (BP) algorithm which includes parameters, learning rate (η), momentum factor (α) and steepness parameter (λ). The appropriate selections of these parameters have large effects on the convergence of the algorithm. Many techniques that adaptively adjust these parameters have been developed to increase speed of convergence. In this paper, we shall present several classes of learning automata based solutions to the problem of adaptation of BP algorithm parameters. By interconnection of learning automata to the feedforward neural networks, we use learning automata scheme for adjusting the parameters η, α, and λ based on the observation of random response of the neural networks. One of the important aspects of the proposed schemes is its ability to escape from local minima with high possibility during the training period. The feasibility of proposed methods is shown through simulations on several problems.

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.

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