WAVELET NEURO-FUZZY MODEL WITH HYBRID LEARNING ALGORITHM OF GRADIENT DESCENT AND GENETIC ALGORITHM

AHMAD BANAKAR; MOHAMMAD FAZLE AZEEM

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WAVELET NEURO-FUZZY MODEL WITH HYBRID LEARNING ALGORITHM OF GRADIENT DESCENT AND GENETIC ALGORITHM

机译：具有梯度遗传和遗传算法的混合学习算法的小波神经模糊模型。

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摘要

In this paper, a Wavelet Neuro-Fuzzy model has been proposed. The proposed work caters an application of wavelet network used in fuzzy systems for forecasting of dynamic systems. A wavelet network approximates the consequent part of each fuzzy rule. The wavelet network is a feed-forward neural network with one hidden layer that uses a combination of Wavelet and Sigmoid Activation Function. A hybrid learning method composed of genetic algorithm and gradient descent is proposed to tune the learning parameters of the proposed Wavelet Neuro-Fuzzy model. Further, an analysis regarding the convergence and stability of gradient descent learning is presented for the proposed Wavelet Neuro-Fuzzy model. To evaluate the effectiveness of proposed model and learning strategy, three different classes of benchmark problems have been considered.

机译：本文提出了一种小波神经模糊模型。拟议的工作迎合了用于动态系统预测的模糊系统中的小波网络的应用。小波网络近似每个模糊规则的结果部分。小波网络是具有一个隐藏层的前馈神经网络，该隐藏层使用了小波和Sigmoid激活函数的组合。提出了一种由遗传算法和梯度下降组成的混合学习方法，以调整所提出的小波神经模糊模型的学习参数。此外，针对提出的小波神经模糊模型，提出了关于梯度下降学习的收敛性和稳定性的分析。为了评估所提出的模型和学习策略的有效性，已经考虑了三类不同的基准问题。

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来源
《International Journal of Wavelets, Multiresolution and Information Processing》 |2011年第2期|共27页
作者
AHMAD BANAKAR; MOHAMMAD FAZLE AZEEM;
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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
Activation network; Wavelet network; Neuro-Fuzzy model; Genetic algorithm; Gradient descent;

机译：激活网络;小波网络;神经模糊模型;遗传算法;梯度下降;

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专利

1. WAVELET NEURO-FUZZY MODEL WITH HYBRID LEARNING ALGORITHM OF GRADIENT DESCENT AND GENETIC ALGORITHM [J] . AHMAD BANAKAR MOHAMMAD FAZLE AZEEM International Journal of Wavelets, Multiresolution and Information Processing . 2011,第2期

机译：具有梯度遗传和遗传算法的混合学习算法的小波神经模糊模型。
2. Some considerations on conventional neuro-fuzzy earning algorithms by gradient descent method [J] . Yan Shi, Masaharu Mizumoto Fuzzy sets and systems . 2000,第1期

机译：常规的梯度下降神经模糊赚钱算法的几点考虑
3. Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm [J] . Khosravi A., Malekan M., Pabon J. J. G., Journal of Cleaner Production . 2020,第PTa1期

机译：基于遗传算法组合优化的自适应神经模糊推理系统的太阳能塔系统设计参数建模与基于教学的优化算法
4. Multi-mother Wavelet Neural Network Training Using Genetic Algorithm-Based Approach to Optimize and Improves the Robustness of Gradient-Descent Algorithms: 3D Mesh Deformation Application [C] . Naziha Dhibi, Chokri Ben Amar International Work-Conference on Artificial Neural Networks . 2019

机译：基于遗传算法的多母波小波神经网络训练优化和提高梯度下降算法的鲁棒性：3D网格变形应用
5. Stochastic Gradient Descent for Modern Machine Learning: Theory, Algorithms and Applications [D] . Kidambi, Rahul. 2019

机译：现代机器学习的随机梯度下降：理论，算法和应用
6. Modeling of paclitaxel biosynthesis elicitation in Corylus avellana cell culture using adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) and multiple regression methods [O] . Siamak Farhadi, Mina Salehi, Ahmad Moieni, 2020

机译：使用自适应神经模糊推理系统 - 遗传算法（ANFIS-GA）和多元回归方法植物植物植物生物合成诱导紫杉醇生物合成诱导的建模
7. Fuzzy clustering, genetic algorithms and neuro-fuzzy methods compared for hybrid fuzzy-first principles modeling [O] . van Lith, Pascal, van Lith, P.F., Betlem, Bernardus H.L., 2002

机译：混合模糊优先原理建模的模糊聚类，遗传算法和神经模糊方法比较
8. Hybrid Conjugate Gradient-Steepest Descent Algorithms for Unconstrained Minimization [R] . Messerli, E. 1968

机译：用于无约束最小化的混合共轭梯度 - 最速下降算法

WAVELET NEURO-FUZZY MODEL WITH HYBRID LEARNING ALGORITHM OF GRADIENT DESCENT AND GENETIC ALGORITHM

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