scholarly journals Prediction of arrival domestic and foreign tourists based on regions using neural network algorithm based on genetic algorithm

2019 ◽  
Vol 1175 ◽  
pp. 012045
Author(s):  
Mohamad Ilyas Abas ◽  
Alter Lasarudin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Sun ◽  
Wenjun Yi ◽  
Dandan Yuan ◽  
Jun Guan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Initial Alignment ◽  
Elman Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

The purpose of this paper is to present an in-flight initial alignment method for the guided projectiles, obtained after launching, and utilizing the characteristic of the inertial device of a strapdown inertial navigation system. This method uses an Elman neural network algorithm, optimized by genetic algorithm in the initial alignment calculation. The algorithm is discussed in details and applied to the initial alignment process of the proposed guided projectile. Simulation results show the advantages of the optimized Elman neural network algorithm for the initial alignment problem of the strapdown inertial navigation system. It can not only obtain the same high-precision alignment as the traditional Kalman filter but also improve the real-time performance of the system.

scholarly journals English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dan Wu ◽  
Yuanjun Shen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Feature Recognition ◽  
Good Effect ◽  
Data Mining Algorithm ◽  
Main Function ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.

scholarly journals A New Optimized GA-RBF Neural Network Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Weikuan Jia ◽  
Dean Zhao ◽  
Tian Shen ◽  
Chunyang Su ◽  
Chanli Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rbf Neural Network ◽  
Lms Algorithm ◽  
Learning Ability ◽  
Operating Efficiency ◽  
Complex Problems ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Hidden Layer

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer’s neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

scholarly journals A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Multilayer Perceptron ◽  
Computer Aided Design ◽  
Machine Learning Algorithms ◽  
Regression Equations ◽  
Market Demand ◽  
Product Modeling ◽  
Network Algorithm ◽  
Neural Network Algorithm

Products no longer exist simply as carriers of useful functions, but more and more consumers are beginning to pay attention to the spiritual aspects of the feelings brought by products. This paper brings machine learning algorithms to the discipline of industrial design and proposes a method to evaluate the design of product shapes using a multilayer perceptron genetic algorithm neural network (GA-MLP-NN) algorithm, quantifying the product shape, using computer-aided design technology to achieve shape optimization, shape, and color scheme generation, and using interactive feedback with users to finally generate a product shape with market demand. In this paper, we use the combinatorial innovation method to arrange and combine the detail elements in the solution library to generate the modeling solution, combine the multilayer perceptron genetic algorithm neural network algorithm with product modeling, and establish the interactive genetic modeling system for the product, use this system to design the product modeling solution, and finally get the product modeling solution satisfied by the target users; using the multilayer perceptron genetic algorithm neural network method to evaluate the product modeling items. The mapping relationship model between morphological feature space and imagery cognitive space was constructed based on multiple linear regression equations, and the multiple regression model for each affective dimension was ideal. The results show that the model performance is reliable. The weights are calculated, and the appropriate people are selected to score and calculate the modeling scheme, and finally, the satisfactory product modeling scheme is obtained.

scholarly journals Application of Neural Network Methods Based on Genetic Algorithm for Breast Cancer Prediction

Tech-E ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Rino -
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
The Body ◽  
Algorithm Optimization ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Risk Of Cancer

Cancer is a major challenge for mankind. Cancer can affect various parts of the body. This deadly disease can be detected in people of all ages. However, the risk of cancer increases with increasing age. Breast cancer is the most common cancer among women, and form largest cause of death for women as well. Then there are problems in the detection of breast cancer, resulting in the patient experiencing unnecessary treatment and cost. Insimilar studies, there are several methods used but there are problems due to the shape of the cancer cells are nonlinear. Neural networks can solve these problems, but neural network is weak in terms of determining the value of the parameter, so it needs to be optimized. Genetic algorithm is one of the optimization methods is good, therefore the values ​​of the parameters of the neural network will be optimized by using a genetic algorithm so as to get the best value of the parameter. Neural Network-based GA algorithm has the higher accuracy value than just using Neural Network algorithm. This is evident from the increase in value for the accuracy of the model Neural Network algorithm by 95.42% and the accuracy of algorithm-based Neural Network algorithm GA (Genetic Algorithm) of 96.85% with a difference of 1.43% accuracy. So it can be concluded that the application of Genetic Algorithm optimization techniques to improve the accuracy values on Neural Network algorithm.

Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys

2007 ◽  
Vol 353-358 ◽  
pp. 1029-1032 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Heng Xi Zhang ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Neural Network Method ◽  
Network Algorithm ◽  
Network Method ◽  
Neural Network Algorithm

In the paper, genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada, and the fatigue performances of pre-corroded aluminum alloys can be predicted. The results indicate that genetic algorithm-neural network algorithm can be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely, compared with traditional neural network.

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