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

scholarly journals Predicting TEC in China based on the neural networks optimized by genetic algorithm

2018 ◽  
Vol 62 (4) ◽  
pp. 745-759 ◽  
Author(s):  
Rui Song ◽  
Xuemin Zhang ◽  
Chen Zhou ◽  
Jing Liu ◽  
Jianhui He
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
The Neural Networks

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.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dimensional Space ◽  
Data Distribution ◽  
High Dimensional ◽  
Sufficient Information ◽  
Sufficient Data ◽  
High Dimensional Space ◽  
Positive Effects ◽  
The Neural Networks ◽  
Using Data

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.

Classification and Recognition of Plant Leaf Based on Neural Networks

2011 ◽  
Vol 464 ◽  
pp. 38-42 ◽  
Author(s):  
Ping Ye ◽  
Gui Rong Weng
Keyword(s):  
Neural Networks ◽  
Fractal Dimension ◽  
Recognition Rate ◽  
Image Binarization ◽  
Characteristic Parameters ◽  
Moment Invariant ◽  
Plant Leaf ◽  
The Neural Networks ◽  
Novel Method ◽  
The Moment

This paper proposed a novel method for leaf classification and recognition. In the method, the moment invariant and fractal dimension were regarded as the characteristic parameters of the plant leaf. In order to extract the representative characteristic parameters, pretreatment of the leaf images, including RGB-gray converting, image binarization and leafstalk removing. The extracted leaf characteristic parameters were further utilized as training sets to train the neural networks. The proposed method was proved effectively to reach a recognition rate about 92% for most of the testing leaf samples

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