scholarly journals A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST

2018 ◽  
Vol 48 (02) ◽  
pp. 481-508 ◽  
Author(s):  
Donatien Hainaut
Keyword(s):  
Neural Network ◽  
Term Structure ◽  
Predictive Power ◽  
Principal Component ◽  
Mortality Rates ◽  
Second Step ◽  
Inverse Transformation ◽  
Latent Factors ◽  
Neural Network Approach ◽  
Mortality Forecast

AbstractThis article proposes a neural-network approach to predict and simulate human mortality rates. This semi-parametric model is capable to detect and duplicate non-linearities observed in the evolution of log-forces of mortality. The method proceeds in two steps. During the first stage, a neural-network-based generalization of the principal component analysis summarizes the information carried by the surface of log-mortality rates in a small number of latent factors. In the second step, these latent factors are forecast with an econometric model. The term structure of log-forces of mortality is next reconstructed by an inverse transformation. The neural analyzer is adjusted to French, UK and US mortality rates, over the period 1946–2000 and validated with data from 2001 to 2014. Numerical experiments reveal that the neural approach has an excellent predictive power, compared to the Lee–Carter model with and without cohort effects.

A Lyapunov Theory-Based Neural Network Approach for Face Recognition

2010 ◽  
pp. 23-48 ◽  
Author(s):  
Li-Minn Ang ◽  
King Hann Lim ◽  
Kah Phooi Seng ◽  
Siew Wen Chin
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Principal Component ◽  
Lyapunov Stability Theory ◽  
Recognition System ◽  
Lyapunov Theory ◽  
Neural Network Approach ◽  
Linear Discriminant ◽  
Feature Based ◽  
Definition Of

This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.

The Influence Function of Principal Component Analysis by Self-Organizing Rule

1998 ◽  
Vol 10 (6) ◽  
pp. 1435-1444 ◽  
Author(s):  
Isao Higuchi ◽  
Shinto Eguchi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Influence Function ◽  
Principal Component ◽  
Component Analysis ◽  
The Self ◽  
Network Approach ◽  
Neural Network Approach ◽  
Principal Component Vector ◽  
Self Organizing

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

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