Hybrid Artificial Neural Network with Genetic Algorithm

版本 2.1.0 (26.7 KB) 作者: Mehdi Ghasri
Optimization of neural network weights and biases using real genetic algorithm
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更新时间 2023/3/21

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Hybrid Artificial Neural Network with Genetic Algorithm
The idea here is to employ the Genetic algorithm to optimize ANN parameters to improve performance. ANN provides the search space and utilizes GA to find the best solution by tuning the weights and biases required to achieve lower error rates. The error between the model output and the exact training data can reach a minimum value by iterating the GA until the desired error is met.
The code is written in such a way that it only needs to change the Excel name in CreateData.m to apply it to any data set:
f=readmatrix('DATA(cylindrical)')
In addition, during the implementation of the code, the parts of the program that require customization by researchers to get the best results from the code are asked in the form of "questdlg".
Researchers can also email the following address for article cooperation in optimization algorithms, various types of neural networks, fuzzy logic, and machine learning.
Email: Eng.mehdighasri@gmail

引用格式

Mehdi Ghasri (2024). Hybrid Artificial Neural Network with Genetic Algorithm (https://www.mathworks.com/matlabcentral/fileexchange/124600-hybrid-artificial-neural-network-with-genetic-algorithm), MATLAB Central File Exchange. 检索来源 .

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版本 已发布 发行说明
2.1.0

Outputs variables:
These two outputs are named below.
Initial values of weights and biases: NotOPT_variable
Final and optimized values of weights and biases: OPT_variable
1.0.0