scholarly journals Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization

2021 ◽  
pp. 471-486
Author(s):  
Oleksiy Kozlov
Keyword(s):  
Information Technology ◽  
Control System ◽  
Ant Colony Optimization ◽  
Fuzzy Systems ◽  
High Efficiency ◽  
Adaptive Fuzzy Control ◽  
Ant Colony ◽  
Ship Steering ◽  
Rule Bases ◽  
Rudder Angle

This paper proposes the universal information technology for designing the rule bases (RB) with the formation of optimal consequents for fuzzy systems (FS) of different types on the basis of ant colony optimization (ACO) techniques. The developed ACO-based information technology allows effectively synthesizing rule bases of various dimensions both for the MISO and MIMO fuzzy systems taking into account the particular features of the RB consequents formation in the conditions of insufficient initial information. In order to study and validate the efficiency of the presented information technology the design of the RB for the adaptive fuzzy control system of the ship steering device is carried out in this work. The computer simulations results show that adaptive control system with developed RB provides achievement of high enough quality indicators of rudder angle control. Thus, application of the proposed ACO-based information technology allows designing effective RB with optimal consequents by means of minor computational costs that, in turn, confirms its high efficiency.

Helicopter Cable-Orientation Adaptive Fuzzy Control System Design Based on Ant Colony Optimization

2014 ◽  
Vol 971-973 ◽  
pp. 511-515
Author(s):  
Xiao Yan Wang ◽  
Xin Min Wang
Keyword(s):  
Control System ◽  
Ant Colony Optimization ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Solution Space ◽  
Ant Colony ◽  
Adaptive Fuzzy ◽  
Variable Universe ◽  
Contraction And Expansion ◽  
Common Variable

Aiming at the complexity of helicopter cable-orientation and high accuracy requirements, a variable universe fuzzy controller combined with ant colony optimization (ACO) is proposed. The ACO is studied and improved to be used in continuous domain. The solution space is decomposed into finite grids then the ACO can be used to optimize the contraction and expansion factors of the universes. The reference cable angle is calculated and the helicopter cable-orientation control system is designed. The adaptive fuzzy controller with variable universe based on ACO is adopted to keep the ground velocity of helicopter at zero. Simulation results indicate that the controller designed in this paper has a better performance than common variable universe fuzzy controller.

scholarly journals INFORMATION TECHNOLOGY FOR SYNTHESIS AND OPTIMIZATION OF FUZZY SYSTEMS RULE BASES

2021 ◽  
pp. 66-74
Author(s):  
Oleksiy Kozlov
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Fuzzy Systems ◽  
Information Technologies ◽  
High Efficiency ◽  
Optimal Number ◽  
Design Rule ◽  
Source Information ◽  
Classification Pattern ◽  
Rule Bases

In the last two decades, intelligent computer systems based on fuzzy set theory, fuzzy logic and soft computing are widely used in various fields of science and technology to solve problems of control, identification, modeling of complex physical and economic phenomena, classification, pattern recognition, etc. Modern research in the field of creation and development of fuzzy systems (FS) of control and decision-making is conducted mainly in the direction of development of highly effective methods and information technologies of their synthesis and structural-parametric optimization. This paper is devoted to the development and research of information technology for the synthesis and optimization of highly efficient rule bases (RB) with the optimal set of consequences and optimal number of rules for Mamdani-type FSs in terms of incomplete source information. The developed information technology allows to conduct iterative search of the optimal vector of RB consequences based on a sequential search of the consequences of each rule, as well as to identify and exclude rules from RB, that do not affect the system operation, to reduce the total number of rules to optimal. To study the effectiveness of the proposed information technology in this work, the synthesis and optimization of the fuzzy automatic control system (ACS) for the multi-purpose mobile robot (MR), which is able to move on inclined and vertical ferromagnetic surfaces, is carried out. The obtained results of computer simulation showed that the fuzzy ACS of MR with optimized RB based on the developed information technology has higher quality indicators of control compared to the ACS with a similar RB developed on the basis of experts knowledge. Also, the optimized RB by means of the proposed information technology has fewer rules than the full RB, which is synthesized on the basis of experts knowledge, which, in turn, significantly simplifies the further software and hardware implementation of the developed fuzzy ACS of MR. In addition, in the process of synthesis and optimization of the RB for the fuzzy ACS of MR, presented information technology did not require significant computational costs, which generally confirms its high efficiency and feasibility of application to design rule bases of control and decision making FSs of different types.

scholarly journals Image retrieval based on swarm intelligence

2021 ◽  
Vol 11 (6) ◽  
pp. 5390
Author(s):  
Shahbaa I. Khaleel ◽  
Ragad W. Khaled
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Water Drop ◽  
Optimization Method ◽  
Modern Technology ◽  
Ant Colony ◽  
Query Image ◽  
Fuzzy Method

To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization with Evaporation

2007 ◽  
Vol 11 (9) ◽  
pp. 1149-1158 ◽  
Author(s):  
Lu Yu ◽  
◽  
Jin Zhou ◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
...  
Keyword(s):  
Control System ◽  
Ant Colony Optimization ◽  
Supervisory Control ◽  
Hybrid Algorithm ◽  
Directed Graphs ◽  
Genetic Network ◽  
Ant Colony ◽  
Network Programming ◽  
Supervisory Control System ◽  
Genetic Network Programming

Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.

Adaptive Fuzzy Control for a SISO Nonlinear System

2003 ◽  
Vol 7 (2) ◽  
pp. 86-91
Author(s):  
Hugang Han ◽  
◽  
Shuta Murakami ◽  
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Tracking Error ◽  
Adaptive Fuzzy Control ◽  
Switching Function ◽  
Adaptive Fuzzy ◽  
Synthesis Approach ◽  
Unknown Case

When using the Lyapunov synthesis approach to construct an adaptive fuzzy control system, one important way is to regard the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled. Concerning the unknownness, generally there are two cases: a completely unknown case, and a partly unknown case. However, most of the schemes presented so far have only focused on the former. Clearly, if an unknown function belongs to the latter, the knowledge available about the function should be utilized as much as possible in the development of the control system. In this paper, our goal is to design an adaptive fuzzy controller for a class of nonlinear systems with uncertainty, which can correspond to the either case. Also, we propose a unique way to deal with the uncertainty, i.e., adopt a switching function with an alterable coefficient, which is tuned by adaptive law based on the tracking error.

Coordinated Ramp Metering Based on Ant Colony Optimization

2011 ◽  
Vol 97-98 ◽  
pp. 990-993
Author(s):  
Xin Rong Liang ◽  
Jian Xiong
Keyword(s):  
Control System ◽  
Ant Colony Optimization ◽  
Traffic Flow ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Macroscopic Model ◽  
Traffic Density ◽  
Model Parameters ◽  
Ramp Control ◽  
Control Layer

Hierarchy control strategy and ant colony optimization are proposed for coordinated ramp control. The macroscopic model to describe the evolution of freeway traffic flow is firstly established. Then the coordinated ramp control system is designed. There are two layers in this coordinated control system: the coordination control layer to select traffic models, to adjust the model parameters, and to determine the desired traffic density in each freeway section according to the current traffic status; and the direct control layer to keep the actual values of state variables in the vicinity of the desired state points via PI controllers. Ant colony algorithm is used to find the optimal PI control parameters. Simulation results show that the control system is of good performance. It can eliminate traffic jams, maintain traffic flow stability, and make vehicles travel more efficiently and safely.

scholarly journals Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Chi-Chung Chen ◽  
Yi-Ting Liu
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Ad Hoc ◽  
Early Stage ◽  
Systems Design ◽  
Population Based ◽  
Ant Colony ◽  
Dynamic Mutation

This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.

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