✦ LIBER ✦

Helicopter flight control with fuzzy logic and genetic algorithms

✍ Scribed by Chad Phillips; Charles L. Karr; Greg Walker

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 791 KB
Volume: 9
Category: Article
ISSN: 0952-1976
DOI: 10.1016/0952-1976(95)00008-9

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Tuning fuzzy logic controllers by geneti

Tuning fuzzy logic controllers by genetic algorithms

✍ F. Herrera; M. Lozano; J.L. Verdegay 📂 Article 📅 1995 🏛 Elsevier Science 🌐 English ⚖ 692 KB

The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control system

Fuzzy logic controller based on genetic

Fuzzy logic controller based on genetic algorithms

✍ Li RenHou; Zhang Yi 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 656 KB

A genetic-algorithm-based method for tun

A genetic-algorithm-based method for tuning fuzzy logic controllers

✍ H.B. Gürocak 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 234 KB

It has been demonstrated many times in practice that fuzzy logic controllers have an important role in rule-based expert systems. However, it is essential for a fuzzy logic controller to have an appropriate set of rules to perform at the desired level. The linguistic structure of the fuzzy logic con

Expert SMC-based fuzzy control with gene

Expert SMC-based fuzzy control with genetic algorithms

✍ Jen-Yang Chen 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 840 KB

Based on sliding mode control (SMC), an expert fuzzy controller synthesized by a collection of fuzzy linguistic control rules is proposed in this paper. The control strategy of the proposed controller is characterized by the linguistic terms in the fuzzy control rules. The membership functions of co

Genetic algorithms for learning the rule

Genetic algorithms for learning the rule base of fuzzy logic controller

✍ T.C. Chin; X.M. Qi 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 432 KB

In this paper, genetic algorithms are used in the study to maximise the performance of a fuzzy logic controller through the search of a subset of rule from a given knowledge base to achieve the goal of minimising the number of rules required. Comparisons are made between systems utilising reduced ru

A self-learning and tuning fuzzy logic c

A self-learning and tuning fuzzy logic controller based on genetic algorithms and reinforcements

✍ Hung-Yuan Chung; Chih-Kuan Chiang 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 237 KB 👁 2 views

This article presents a new method for learning and tuning a fuzzy logic controller automatically. A reinforcement learning and a genetic algorithm are used in conjunction with a multilayer neural network model of a fuzzy logic controller, which can automatically generate the fuzzy control rules and