Optimization Design of Fuzzy Control Rules Based on Ant Colony Algorithm

2014 ◽  
Vol 716-717 ◽  
pp. 1662-1665
Author(s):  
Ya Lang Xing ◽  
He Xin ◽  
Jin Cheng Zhao
Keyword(s):  
Fuzzy Control ◽  
Optimization Design ◽  
Ant Colony Algorithm ◽  
Fuzzy Rule ◽  
Coefficient Matrix ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Control Rules ◽  
And Control

To avoid the fuzzy rules getting into “rule exploding” in fuzzy control system, a fuzzy control rules optimization algorithm based on compatibility coefficient is proposed. The method defines the compatibility coefficient of fuzzy rules, and the compatibility coefficient matrix is used to be the heuristic information in ant colony algorithm. Ant colony algorithm is used to optimize designed complete fuzzy rule base. Simulation results show that the fuzzy rules have good compatibility and control performance.

Optimal Design for Fuzzy Controller by Ant Colony Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 1190-1196 ◽  
Author(s):  
Bao Jiang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Real Time Control ◽  
Time Control ◽  
Information Updating ◽  
Fuzzy Logical

Fuzzy logical controller is one of the most important applications of fuzzy-rule-based system that models the human decision processing with a collection of fuzzy rules. In this paper, an adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of selection of the paths and the strategy of the trail information updating. The algorithm is used to design a fuzzy logical controller automatically for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due to multivariable inputs, state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. Experimental results show that the designed controller can control actual inverted pendulum successfully.

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

scholarly journals Optimization design of reconfiguration algorithm for high voltage power distribution network based on ant colony algorithm

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1094-1106 ◽  
Author(s):  
Zhen Li ◽  
Yao Zhang ◽  
Muhammad Aqeel Ashraf
Keyword(s):  
High Voltage ◽  
Power Distribution ◽  
Optimization Design ◽  
Power Flow ◽  
Ant Colony Algorithm ◽  
Distribution Network ◽  
Network Reconstruction ◽  
Ant Colony ◽  
Power Distribution Network ◽  
Power Flow Calculation

Abstract Distribution network reconfiguration is a very complex and large-scale combinatorial optimization problem. In network reconfiguration, whether an effective solution can be obtained is a key issue. Aiming at the problems in network reconstruction by traditional algorithm, such as long time required, more times of power flow calculation and high network loss, a network optimization design algorithm based on improved ant colony algorithm for high voltage power distribution network is proposed. After analyzing the operating characteristics of the high voltage power distribution network, the network topology of the high voltage power distribution network is described by constructing a hierarchical variable-structure distribution network model. A mathematical model of distribution network reconstruction considering the opportunity constraint with the minimum network loss as the objective function is established. The power flow distribution is calculated by using the pre-push back-generation method combined with the hierarchical structure of the distribution network. The maximum and minimum ant colony algorithm is introduced to improve the pheromone updating method of the traditional ant colony algorithm, and the search range is expanded, so that the algorithm can jump out of the local optimization trap to realize the accurate solution of the power distribution network reconstruction model. The experimental results show that compared with the current network reconstruction algorithm, the proposed algorithm requires less time for convergence, less power flow calculation, and lower network loss.

Optimization Design of Self-Balacing Torsion Bar Diameter

2011 ◽  
Vol 308-310 ◽  
pp. 2486-2489
Author(s):  
Zhi Qi Huang
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Ant Colony Algorithm ◽  
The Self ◽  
Ant Colony ◽  
New Method ◽  
C Language ◽  
Torsion Bar ◽  
Bar Diameter ◽  
Theoretical Results

The thesis builds the optimization model for the self-balacing torsion bar, On the basis of the Ant Colony Algorithm, designs the Ant Colony Algorithm procedure using C Language and optimizes torsion bar diameter. Results show the Ant Colony Algorithm is feasible and provides a new method choosing torsion bar diameter. The max difference value is 1.12% between optimizing results and theoretical results.

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

scholarly journals Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

2020 ◽  
Vol 10 (1) ◽  
pp. 57-69 ◽  
Author(s):  
Marcin Korytkowski ◽  
Roman Senkerik ◽  
Magdalena M. Scherer ◽  
Rafal A. Angryk ◽  
Miroslaw Kordos ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Differential Evolution Algorithm ◽  
Fuzzy Rule ◽  
Image Features ◽  
Population Diversity ◽  
Fuzzy Rules ◽  
Support Vector ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Novel Method

AbstractFast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

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