Neural network error Back Propagation

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly
i, aj is the activation of Background:
This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method.
A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models.
The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The

Source: Nutrition Journal - Latest Articles

Cerebrovascular accident attack classification using multilayer feed forward artificial neural network with back propagation error.(Report): An article from: Journal of Computer Science
Book (Science Publications)

You might also like:

Visualize Back Propagation: (4) Weight-space
Visualize Back Propagation: (4) Weight-space
Lec-19 Back Propagation Algorithm
Lec-19 Back Propagation Algorithm
Neural Network Tutorial - Ch. 6.1 Training the network
Neural Network Tutorial - Ch. 6.1 Training the network

Your child is expected to be

2011-03-21 05:40:32 by dixiessmom

Supported by both parents. However, remember the other parent is supporting the child when she is there. They are feeding the child,paying for activities etc. Whether they have to provide any money to you depends on many factors.You should google the child support calculator for CA.I would first try to work out a parenting plan. If the child can only see the parent once a month,what can you offer in exchange? more time over the summer? extra school breaks? This is not a drastic change but be ready to make up for the lost time. Also, let the other parent know that any time they want to come to CA they are welcome and can go to the activities with the child

Payline by ICE Plans Job Growth in Victoria in 2012  — MarketWatch
VICTORIA, BRITISH COLUMBIA, Apr 24, 2012 (MARKETWIRE via COMTEX) -- Payline Financial has partnered with ICE - International Currency Exchange (ICE) to become Payline by ICE. Payline by ICE growth plans include branching out nationally and adding more …

BUSINESS BEAT: Connect Hearing named a top workplace  — Goldstream News Gazette
Payline Financial has partnered with International Currency Exchange (ICE) to become Payline by ICE, offering secure online foreign exchange services and personalized account management for individuals and corporate clients.

Airport to award currency exchange contract to different firm  — Atlanta Journal Constitution
The city acknowledged in January that the winning firm it selected last year, International Currency Exchange, had a subcontractor that did not meet the requirements for experience. The subcontractor, Paracom, also lacked a certificate to operate in …

Neuronal Network back-propagation Learning : Robot Avoider C++ / Qt
Neuronal Network back-propagation Learning : Robot Avoider C++ / Qt
Neural Network Tutorial - Ch. 1 Back Propagation library
Neural Network Tutorial - Ch. 1 Back Propagation library

Related posts:

  1. Neural Network Backpropagation PPT
  2. Neural network for stock prediction
  3. Neural Network Backpropagation Code
  4. Neural Network Backpropagation c
  5. Neural networks with Backpropagation