Quick Design of Fuzzy Controllers With Good Interpretability in Mobile Robotics

Mucientes, Manuel; Casillas, Jorge

doi:10.1109/tfuzz.2006.889889

Cited by 67 publications

(34 citation statements)

References 38 publications

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“…ACA has recorded huge success when used to solve other combinatorial problems including graph coloring [42], data mining and optimization [59], [160], Machine learning [48], [49], [69], [141], [158], [165] mobile robots [31], [155], network routing and load-balancing [193], vehicle routing [55], fuzzy controller [108], quadratic assignment [135] and the shortest common super-sequence problem [145]. Although, a global behavior emerges, through these simple local interactions between individual ants since each of them possesses only limited skill and communication capability.…”

Section: Ant Colony Algorithm (Aca)mentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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Due to notable technological convergence that brought about exponential growth in computer world, SoftComputing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

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Section: Ant Colony Algorithm (Aca)mentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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“…Therefore, in order to start the control of the plant using very limited information, we need to use advanced adaptive skills that exceed the aforementioned adaptive process, i.e., more internal parameters of the used controller need to be adapted and optimized. Nevertheless, important number of works that appear in the literature present adaptive controllers with prefixed structure [20,21,22], hence, there are some risks that the plant may not be adequately represented [19]. The methodology suggested in this work is based on the use of large amount of data provided from the real behavior of the control process; the Input/output data extracted in real time can reflect important information about the real inverse function of the controlled plant.…”

Section: ͵ǥ͵ǥʹǥ ǧǣmentioning

confidence: 99%

A New Adaptive and Self Organizing Fuzzy Policy to Enhance the Real Time Control Performance

Lasri

Rojas

Pomares

et al. 2014

IJCIS

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In this paper, a temperature control in real time control process was presented using several control algorithms. A quantitative comparison based on the real power consumption and (the precision and the robustness) of these controllers during the same control process and under the same conditions will be done. The proposed Adaptive and Self Organizing Fuzzy policy has been able to prove its superiority against the remaining controllers. The new Adaptive and Self Organizing Fuzzy Logic Controller starts the control with a very limited information about the controlled process (delay and the monotonicity sign) and without any kind of offline pre-training, the adaptive controller acts online to collect the necessary background to adapt their rules consequents and to self organize their membership functions from the real behavior of the controlled process. During 200 minutes and under the same conditions all the performed controllers have been used to control the room temperature, each simulation has been repeated five times with two different sets of set points. These amounts of results was used as a set of sampling for the statistical tool ANOVA (Analysis of Variance) that can prove and illustrate the validity and the extrapolability of the conclusions extracted from several stages of this work.

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“…The behavior is not specially difficult and has been successfully addressed by some supervised-learning-based methods (e.g., [34]). What really makes this problem difficult is to perform online learning.…”

Section: Realistic Mobile Robot Problemmentioning

confidence: 99%

“…This robot is provided by a ring of 16 ultrasound sensors. The four considered input variables are calculated from this information as follows [34]. Two of the input variables are the relative right-hand distance and the distance quotient , which are calculated as RD right-hand distance…”

Section: ) Input and Output Variablesmentioning

confidence: 99%

Fuzzy-XCS: A Michigan Genetic Fuzzy System

Casillas

Carse

Bull

2007

IEEE Trans. Fuzzy Syst.

Self Cite

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Abstract-The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform online reinforcement learning by means of Michigan-style fuzzy rule systems, this issue becomes even more difficult. Indeed, rule generalization (description of state-action relationships with rules as compact as possible) has received a great attention in the nonfuzzy evolutionary learning field (e.g., XCS is the subject of extensive ongoing research). However, the same issue does not appear to have received a similar level of attention in the case of Michigan-style fuzzy rule systems. This may be due to the difficulty in extending the discrete-valued system operation to the continuous case. The intention of this contribution is to propose an approach to properly develop a fuzzy XCS system for single-step reinforcement problems.Index Terms-Continuous action, genetic fuzzy systems, Michigan-style learning classifier systems, reinforcement learning.

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Quick Design of Fuzzy Controllers With Good Interpretability in Mobile Robotics

Cited by 67 publications

References 38 publications

A Holistic Review of Soft Computing Techniques

A Holistic Review of Soft Computing Techniques

A New Adaptive and Self Organizing Fuzzy Policy to Enhance the Real Time Control Performance

Fuzzy-XCS: A Michigan Genetic Fuzzy System

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