scholarly journals Modeling of Duck Density and Complex Stocking Time in Rice-Duck Agroecosystems in Terms of Economic and Ecological Benefits

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dahong Xiong ◽  
Kui Fang ◽  
Ying Luo ◽  
Xiaopeng Dai
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Quantitative Assessment ◽  
Intelligent Algorithm ◽  
Ecological Benefits ◽  
Hybrid Intelligent Algorithm ◽  
Numerical Examples ◽  
The Neural Networks ◽  
Fitting Ability ◽  
Selection Of

Rice-duck integrated farming is an effective step under today’s sustainable development background. To make better economic and ecological benefits, a rice-duck agroecosystem is established and kept, in which the paddy field, rice, and the duck mutually promote one another. But the duck density and complex stocking time must be rationally selected. Aiming to attain quantitative assessment and optimal selection of the duck density and complex stocking time in this kind of systems, a methodology based on proposed mathematical models in terms of comparative economic and ecological benefits is addressed. Then the models are solved by a hybrid intelligent algorithmNN-GAthat integrates the Neural Networks (NN) and Genetic Algorithm (GA), making use of the fitting ability in nonlinear fitness context of Neural Networks and the optimization ability of the Genetic Algorithm. Besides, numerical examples are demonstrated in order to test the proposed models. Results reveal that the methodology is reasonable and feasible.

Research of Intelligent Control of Traffic Signal

2014 ◽  
Vol 644-650 ◽  
pp. 107-111
Author(s):  
Xiang Li ◽  
Jin Song Du ◽  
Jing Tao Hu ◽  
Xin Bi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Intelligent Control ◽  
Traffic Signal ◽  
Future Research ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Multi Agent

At present, in the field of intelligent control of traffic signal, most of scholars at home and abroad use fuzzy control and intelligent algorithm, such as genetic algorithm, ant colony optimization, particle swarm optimization, multi-agent, artificial neural networks, fuzzy method etc. This paper summarizes and analyzes these algorithms, points out the problems and shortcomings in the present research, puts forward the direction and trend in the future research. These works have certain directive significance to the research and development of intelligent control of traffic signal.

Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks

2001 ◽  
Vol 5 (5) ◽  
pp. 300-305
Author(s):  
Wimpie D. Nortje ◽  
◽  
Johann E. W. Holm ◽  
Gerhard P. Hancke ◽  
Imre. J. Rudas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Bayesian Networks ◽  
Training Data ◽  
Computational Time ◽  
Bayesian Techniques ◽  
Multiple Networks ◽  
The Neural Networks ◽  
Computational Errors ◽  
Bias Variance ◽  
Selection Of

Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tedious in terms of computational time. It is for this reason that alternatives to Bayesian networks are investigated.

Predicting foF2 in the China region using the neural networks improved by the genetic algorithm

2013 ◽  
Vol 92 ◽  
pp. 7-17 ◽  
Author(s):  
Ruopeng Wang ◽  
Chen Zhou ◽  
Zhongxin Deng ◽  
Binbin Ni ◽  
Zhengyu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
The Neural Networks ◽  
China Region

Induction Motor Direct Torque Control Based on Multiple Neural Networks Optimized by Genetic Algorithm

2012 ◽  
Vol 482-484 ◽  
pp. 1985-1989
Author(s):  
Gan Zou ◽  
Tao Li ◽  
Ren Xin Xiao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Simulation Model ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Speed Sensor ◽  
Multiple Neural Networks ◽  
The Neural Networks ◽  
Simulation Results

Conventional direct torque control(DTC) of induction motor has the problem of large torque ripple.In addition,the speed sensor has its deficiency.A novel DTC system based on multiple neural networks optimized by Genetic Algorithm is proposed and the structures of the proposed system are designed.Genetic algorithm was used to optimize the initial weights and thresholds of the neural networks,All parameters of the neural networks were obtained by offline training.A simulation model of induction motor DTC system was developed in Matlab／Simulink,the simulation results show the feasibility and effectiveness of the scheme

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