An intelligent control system with a multi-objective self-exploration process

Liang-Hsuan Chen, Cheng Hsiung Chiang

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System - mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model.

Original languageEnglish
Pages (from-to)275-294
Number of pages20
JournalFuzzy Sets and Systems
Volume143
Issue number2
DOIs
Publication statusPublished - 2004 Apr 16

Fingerprint

Intelligent Control
Intelligent control
Intelligent Systems
Control System
Control systems
Controllers
Fuzzy neural networks
Fuzzy rules
Motion planning
Simulated annealing
Mobile robots
Artificial intelligence
Robotics
Generator
Genetic algorithms
Receptor
Controller
Pareto Optimality
Multi-objective Genetic Algorithm
Fuzzy Neural Network

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Electrical and Electronic Engineering
  • Statistics, Probability and Uncertainty
  • Information Systems and Management
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence

Cite this

@article{2c52ca681dd44045b64d8410b211fc73,
title = "An intelligent control system with a multi-objective self-exploration process",
abstract = "This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System - mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model.",
author = "Liang-Hsuan Chen and Chiang, {Cheng Hsiung}",
year = "2004",
month = "4",
day = "16",
doi = "10.1016/S0165-0114(03)00183-0",
language = "English",
volume = "143",
pages = "275--294",
journal = "Fuzzy Sets and Systems",
issn = "0165-0114",
publisher = "Elsevier",
number = "2",

}

An intelligent control system with a multi-objective self-exploration process. / Chen, Liang-Hsuan; Chiang, Cheng Hsiung.

In: Fuzzy Sets and Systems, Vol. 143, No. 2, 16.04.2004, p. 275-294.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An intelligent control system with a multi-objective self-exploration process

AU - Chen, Liang-Hsuan

AU - Chiang, Cheng Hsiung

PY - 2004/4/16

Y1 - 2004/4/16

N2 - This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System - mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model.

AB - This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System - mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model.

UR - http://www.scopus.com/inward/record.url?scp=1642303325&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1642303325&partnerID=8YFLogxK

U2 - 10.1016/S0165-0114(03)00183-0

DO - 10.1016/S0165-0114(03)00183-0

M3 - Article

VL - 143

SP - 275

EP - 294

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

SN - 0165-0114

IS - 2

ER -