Fuzzy rule base derivation using neural network-based fuzzy logic controller by self-learning

Lee, B.H.

doi:10.1109/iecon.1993.339038

Cited by 15 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to evaluate our new genetic reduction and learning-based control algorithm, we compare it against (i) neuro-fuzzy/fuzzy methods, [38][39][40][41][42][43] (ii) NonFuzzy methods: SMC and PD-PID controller 44 We compare proposed model against well-known reinforcement learning method called Symbiotic, Adaptive Neuro-Evolution (SANE) 38 and other existing methods. [39][40][41][42][43] The control system for cart-pole inverted system is swing up and stabilized within a few seconds or time steps in several studies including.…”

Section: Evaluating Our Studymentioning

confidence: 99%

Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms

Shill

Akhand

Asaduzzaman

et al. 2015

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs) through real and binary coded coupled genetic algorithms (GAs). The adaptive schema is divided into two phases: the¯rst phase is concerned with optimizing the FLCs membership functions and second phase called rule learning and reducing phase which automatically generates the fuzzy rules as well as determines the minimum number of rules required for building the fuzzy models. In the second phase, the redundant rules are removed by setting their all consequent weight factor to zero and merging the con°icting rules during the learning process. The¯rst and second phases are carried out by the real and binary coded coupled GAs, respectively. Optimizing the MFs with learning and reducing rule base concurrently represents a way to maximize the performance of a FLC. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits the better or competitive performance of the proposed method when compared with the existing methods.

show abstract

Section: Evaluating Our Studymentioning

confidence: 99%

Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms

Shill

Akhand

Asaduzzaman

et al. 2015

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

show abstract

“…It took more than 12.0s,however, to asymptotically stabilize the pendulum system with some offset besides its structure complexity. Kyung and Lee [3] built a fuzzy controller, whose rule base was derived from three neural networks. Although the fuzzy controller stabilized a pendulum system in about 8.0s,it needed 396 rules even after a smoothing procedure and a logical reduction procedure.…”

Section: Imentioning

confidence: 99%

The designation of A T-S fuzzy controller based single input

Gao

2011

2011 IEEE International Conference on Computer Science and Automation Engineering

View full text Add to dashboard Cite

To solve rule blast of inverted pendulum fuzzy controller, which is multi-input and single-output, a multi-dimensional controller is decomposed into a lot of one-dimensional controllers in this paper. The method can reduce the number of fuzzy control rules greatly, and decrease the computing time of controller. A single inverted pendulum fuzzy control system was designed by the suggested method .The curves of experiments show the method is effective.

show abstract

“…The family of inverted pendulum systems can be classified into single inverted pendulum system [3,6], parallel-type double inverted pendulum systems [1,2], series-type double inverted pendulum systems [4],and so on. Recently, a lot of researches on stabilization control of double inverted pendulum systems by using fuzzy inferences have been done.…”

Section: Introductionmentioning

confidence: 99%

A new fuzzy controller for stabilization of double inverted pendulum system

Zhang

Shao

et al. 2010

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering

View full text Add to dashboard Cite

A new fuzzy controller for stabilization control of double inverted pendulum systems is presented based on the fusion function. By introducing comprehensive error and comprehensive error rate, reducing the number of input variables, so as to solve the problem of explosion of fuzzy rules. The total number of fuzzy rules can be reduced significantly and the fuzzy rules can be easily established. Simulation results show that the proposed fuzzy controller can stabilize completely a double inverted pendulum system within 5.0 s for different initial direction of the two pendulums angle and has strong anti-interference ability, robustness and good features.

show abstract

Fuzzy rule base derivation using neural network-based fuzzy logic controller by self-learning

Cited by 15 publications

References 9 publications

Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms

Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms

The designation of A T-S fuzzy controller based single input

A new fuzzy controller for stabilization of double inverted pendulum system

Contact Info

Product

Resources

About