Self-learning fuzzy logic control in medicine

1997 ◽  
pp. 300-303 ◽  
Author(s):  
D. G. Mason ◽  
D. A. Linkens ◽  
N. D. Edwards
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Logic Control ◽  
Self Learning

Self-learning fuzzy logic control of anaesthetic drug infusions

1998 ◽  
Author(s):  
D.G. Mason ◽  
D.A. Linkens ◽  
N.D. Edwards ◽  
J.J. Ross
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Anaesthetic Drug ◽  
Logic Control ◽  
Self Learning

An Interleaved Equalization Architecture with Self-Learning Fuzzy Logic Control for Series-Connected Battery Strings

2017 ◽  
Vol 66 (12) ◽  
pp. 10923-10934 ◽  
Author(s):  
Chenghui Zhang ◽  
Yunlong Shang ◽  
Zeyuan Li ◽  
Naxin Cui
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Logic Control ◽  
Self Learning

A portable self‐learning fuzzy logic control system for muscle relaxation

Anaesthesia ◽  
1998 ◽  
Vol 53 (2) ◽  
pp. 136-139 ◽  
Author(s):  
N. D. Edwards ◽  
D. G. Mason ◽  
J. J. Ross
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Muscle Relaxation ◽  
Logic Control ◽  
Self Learning

scholarly journals Self-learning fuzzy logic control of neuromuscular block

1997 ◽  
Vol 78 (4) ◽  
pp. 412-415 ◽  
Author(s):  
J J Ross ◽  
D G Mason ◽  
D A Linkens ◽  
N D Edwards
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Neuromuscular Block ◽  
Logic Control ◽  
Self Learning

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

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