Mathematical models for response to amino acids: estimating the response of broiler chickens to branched-chain amino acids using support vector regression and neural network models

2017 ◽  
Vol 30 (8) ◽  
pp. 2499-2508 ◽  
Author(s):  
A. Gitoee ◽  
A. Faridi ◽  
J. France
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Mathematical Models ◽  
Support Vector Regression ◽  
Broiler Chickens ◽  
Network Models ◽  
Branched Chain Amino Acids ◽  
Support Vector ◽  
Neural Network Models ◽  
Branched Chain

Bootstrapped neural network models for analyzing the responses of broiler chicks to dietary protein and branched chain amino acids

2014 ◽  
Vol 94 (1) ◽  
pp. 79-85 ◽  
Author(s):  
A. Faridi ◽  
A. Golian ◽  
A. Heravi Mousavi ◽  
J. France
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Dietary Protein ◽  
Average Daily Gain ◽  
Network Models ◽  
Branched Chain Amino Acids ◽  
Broiler Chicks ◽  
Neural Network Models ◽  
Isoleucine Leucine ◽  
Branched Chain

Faridi, A., Golian, A., Heravi Mousavi, A. and France, J. 2014. Bootstrapped neural network models for analyzing the responses of broiler chicks to dietary protein and branched chain amino acids. Can. J. Anim. Sci. 94: 79–85. Reliable prediction of avian responses to dietary nutrients is essential for planning, management, and optimization activities in poultry nutrition. In this study, two bootstrapped neural network (BNN) models, each containing 100 separated neural networks (SNN), were developed for predicting average daily gain (ADG) and feed efficiency (FE) of broiler chicks in response to intake of protein and branched chain amino acids (BCAA) in the starter period. Using a re-sampling method, 100 different batches of data were generated for both the ADG and FE sets. Starting with 270 data lines extracted from eight studies in the literature, SNN models were trained, tested, and validated with 136, 67, and 67 data lines, respectively. All 200 SNN models developed, along with their respective BNN ones, were subjected to optimization (to find the optimum dietary protein and BCAA levels that maximize ADG and FE). Statistical analysis indicated that based on R 2, the BNN models were more accurate in 76 and 56 cases (out of 100) compared with the SNN models developed for ADG and FE, respectively. Optimization of the BNN models showed protein, isoleucine, leucine, and valine requirements for maximum ADG were 231.80, 9.05, 14.03 and 10.90 g kg−1 of diet, respectively. Also, maximum FE was obtained when the diet contained 232.30, 9.07, 14.50, and 11.04 g kg−1 of protein, isoleucine, leucine, and valine, respectively. The results of this study suggest that in meta-analytic modelling, bootstrap re-sampling algorithms should be used to better analyze available data and thereby take full advantage of them. This issue is of importance in the animal sciences as producing reliable data is both expensive and time-consuming.

Comparisons of Support Vector Regression and Neural Network in Modelling the Hydraulic Damper

2011 ◽  
Vol 403-408 ◽  
pp. 3805-3812 ◽  
Author(s):  
Kong Hui Guo ◽  
Xian Yun Wang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Support Vector Regression ◽  
Network Models ◽  
Support Vector ◽  
Neural Network Models ◽  
Hydraulic Damper ◽  
Nonparametric Models ◽  
Force Velocity ◽  
Bpnn Model

Nonparametric models of hydraulic damper based on support vector regression (SVR) are developed. Then these models are compared with two kinds neural network models. One is backpropagation neural network (BPNN) model; another is radial basis function neural network (RBFNN) model. Comparisons are carried out both on virtual damper and actual damper. The force-velocity relation of a virtual damper is obtained based on a rheological model. Then these data are used to identify the characteristics of the virtual damper. The dynamometer measurements of an actual displacement-dependent damper are obtained by experiment. And these data are used to identify the characteristics of this actual damper. The comparisons show that BPNN model is best at identifying the characteristics of the virtual damper, but SVR model is best at identifying the characteristics of the actual damper. The reason is that all experimental data include noise more or less. When the amplitude of the noise is smaller than the parameter of SVR, the noise can not affect the construction of the resulting model. So when training a model based on the experimental data, SVR is superior to other neural networks methods.

scholarly journals HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8184
Author(s):  
Tian Gao ◽  
Anil Kumar Nalini Chandran ◽  
Puneet Paul ◽  
Harkamal Walia ◽  
Hongfeng Yu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Seed Quality ◽  
Network Models ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Neural Network Models ◽  
Systematic Analysis ◽  
Background Removal ◽  
End To End

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.

A review and classification of the existing models of cyanobacteria

2006 ◽  
Vol 30 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Basak Guven ◽  
Alan Howard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Population Dynamics ◽  
Mathematical Models ◽  
Network Models ◽  
Transport Mechanisms ◽  
Neural Network Models ◽  
The World ◽  
Artificial Neural Network Models

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting bloom occurrence in lakes and rivers. In this paper existing key models of cyanobacteria are reviewed, evaluated and classified. Two major groups emerge: deterministic mathematical and artificial neural network models. Mathematical models can be further subcategorized into those models concerned with impounded water bodies and those concerned with rivers. Most existing models focus on a single aspect such as the growth of transport mechanisms, but there are a few models which couple both.

scholarly journals Forecasts of Tropical Pacific Sea Surface Temperatures by Neural Networks and Support Vector Regression

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Silvestre Aguilar-Martinez ◽  
William W. Hsieh
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Water Volume ◽  
Support Vector ◽  
Neural Network Models ◽  
Error Norm ◽  
Sst Anomalies ◽  
The Tropical Pacific

Two nonlinear regression methods, Bayesian neural network (BNN) and support vector regression (SVR), and linear regression (LR), were used to forecast the tropical Pacific sea surface temperature (SST) anomalies at lead times ranging from 3 to 15 months, using sea level pressure (SLP) and SST as predictors. Datasets for 1950–2005 and 1980–2005 were studied, with the latter period having the warm water volume (WWV) above the 20∘C isotherm integrated across the equatorial Pacific available as an extra predictor. The forecasts indicated that the nonlinear structure is mainly present in the second PCA (principal component analysis) mode of the SST field. Overall, improvements in forecast skills by the nonlinear models over LR were modest. Although SVR has two structural advantages over neural network models, namely (a) no multiple minima in the optimization process and (b) an error norm robust to outliers in the data, it did not give better overall forecasts than BNN. Addition of WWV as an extra predictor generally increased the forecast skills slightly; however, the influence of WWV on SST anomalies in the tropical Pacific appears to be linear.

Sign in / Sign up

Export Citation Format

Share Document