A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm

Neety Bansal; Parvinder Kaur

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A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm

机译：基于Bat算法的模糊模型辨识新方法。

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摘要

The identification of a fuzzy model is a complex and nonlinear problem. This can be formulated as a search and optimisation problem and many computing approaches are available in the literature to solve this problem. This research paper is focused on using a new nature inspired approach for fuzzy modeling based on Bat Algorithm which is derived from the behaviour of micro-bats to search for their prey. The bat algorithm approach has been implemented and validated successfully on a rapid battery charger fuzzy controller problem. Currently, the key requirement is real-time solutions to complex problems at a blazing speed. Bat algorithm evolved the optimised fuzzy model within a few seconds as compared to other approaches.

机译：模糊模型的识别是一个复杂的非线性问题。这可以表述为搜索和优化问题，文献中提供了许多计算方法来解决该问题。本文的研究重点是基于新的自然启发方法，基于蝙蝠算法进行模糊建模，该算法是从微型蝙蝠的行为中寻找猎物的。蝙蝠算法方法已在快速电池充电器模糊控制器问题上实现并成功验证。当前，关键的要求是以惊人的速度实时解决复杂问题。与其他方法相比，蝙蝠算法可在几秒钟内演化出优化的模糊模型。

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来源
《International journal of geotechnical earthquake engineering》 |2019年第2期|93-108|共16页
作者
Neety Bansal; Parvinder Kaur;
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作者单位

Maharishi Markandeshwar University, Haryana, India;

Chandigarh College of Engineering and Technology, Degree Wing, Chandigarh;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Bat Algorithm; Fuzzy Controller; Fuzzy Logic; Fuzzy Model Identification; Genetic Algorithms; Nature Inspired Approaches; Neural Networks; Soft Computing;

机译：蝙蝠算法;模糊控制器;模糊逻辑;模糊模型识别;遗传算法;自然启发方法;神经网络;软计算;

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专利

1. A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm [J] . Neety Bansal, Parvinder Kaur International journal of geotechnical earthquake engineering . 2019,第2期

机译：一种基于BAT算法的模糊模型识别的一种新方法
2. A new approach for multimodel identification of complex systems based on both neural and fuzzy clustering algorithms [J] . Nesrine Elfelly, Jean-Yves Dieulot, Mohamed Benrejeb, Engineering Applications of Artificial Intelligence . 2010,第7期

机译：基于神经和模糊聚类算法的复杂系统多模型辨识新方法
3. Robust T-S Fuzzy Model Identification Approach Based on FCRM Algorithm and L1-Norm Loss Function [J] . Zhang Nan, Xue Xiaoming, Xia Xin, Quality Control, Transactions . 2020,第期

机译：基于FCRM算法的鲁棒T-S模糊模型识别方法和L1 - 规范损失函数
4. Nonlinear Dynamical Systems Modelling and Identification Using Type-2 Fuzzy Logic. Metaheuristic Algorithms Based Approach. [C] . Mohammed Assam OUALI, Mohamed LADJAL International Conference on Electrical Engineering . 2020

机译：使用类型2模糊逻辑的非线性动力系统建模和识别。基于元启发式算法的方法。
5. An Application of Neuro-fuzzy Algorithmic Modeling for State of Charge and State of Health Estimation for Sodium-ion Batteries [D] . Fesmire, Jacob C. 2018

机译：神经模糊算法建模在钠离子电池的充电状态和健康估算状态的应用
6. Inflation rate modeling: Adaptive neuro-fuzzy inference system approach and particle swarm optimization algorithm (ANFIS-PSO) [O] . Fateme Nazari Robati, Saeed Iranmanesh 2020

机译：通货膨胀率建模：自适应神经模糊推理系统方法和粒子群优化算法（ANFIS-PSO）
7. Robust T-S Fuzzy Model Identification Approach Based on FCRM Algorithm and L1-Norm Loss Function [O] . Nan Zhang, Xiaoming Xue, Xin Xia, 2020

机译：基于FCRM算法的鲁棒T-S模糊模型识别方法和L1 - 规范损失函数

A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm

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