Induction of fuzzy rules and membership functions from training examples

Hong, Tzung-Pei; Lee, Chai-Ying

doi:10.1016/0165-0114(95)00305-3

Cited by 345 publications

(141 citation statements)

References 11 publications

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“…In our system, the input and output fuzzy variables (E, ΔE, and U s1 ) have a set of seven linguistic terms {NB, NM, NS, ZE, PS, PM, PB}. The membership functions of these variables are defined of triangle shapes because of its simplification and effectiveness [28] as represented in Figure 9, 10. The fuzzy rules are constructed based on the robot itself and the expert experience, which is actually a cut and trial procedure.…”

Section: Figure 8 the Fuzzy Steering Controllermentioning

confidence: 99%

Vision Based Autonomous Path/Line Following of a Mobile Robot Using a Hybrid Fuzzy PID Controller

Thuy¹,

Cường²

2016

Tuyển Tập Công Trình HNKH Toàn Quốc Lần Thứ 3 Về Điều Khiển &Amp; Tự Động Hoá VCCA - 2015

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Hội nghị toàn quốc lần thứ 3 về Điều khiển và Tự động hoá -VCCA-2015 VCCA-2015Rôbốt di động bám quỹ đạo tự động dựa trên xử lý ảnh sử dụng bộ điều khiển lai PID-mờ Tóm tắtTrong bài báo này, chúng tôi đề xuất một phương pháp điều khiển cho robốt tự hành để bám quỹ đạo cho trước trong môi trường trong nhà. Để bám được theo quỹ đạo, các kỹ thuật sử dụng logíc mờ với thông tin đầu vào từ hệ thống xử lý ảnh đã được nghiên cứu và áp dụng. Phương pháp điều khiển sử dụng trong hệ thống là sự kết hợp giữa bộ điều khiển lái -bộ điều khiển lai PID-mờ, và bộ điều khiển vận tốc tuyến tính -bộ điều khiển mờ. Để khẳng định tính đúng đắn của giải thuật điều khiển, chúng tôi đã tiến hành thử nghiệm trên một robốt tự hành, robốt thiết kế dựa trên cơ sở các linh kiện sẵn có trên thị trường. Thử nghiệm chỉ ra rằng, robốt sử dụng giải thuật chúng tôi đề xuất bám tốt theo quỹ đạo cho trước -bao gồm cả quỹ đạo cong và quỹ đạo thẳng. Từ khóa: Hệ thống điều khiển, robốt tự hành, hệ thống xử lý ảnh, logíc mờ, bộ điều khiển lái, bộ điều khiển tốc độ. AbstractIn this work, we propose a method for autonomous path/line following of differential-drive wheeled robot to track straight and curved paths in an indoor environment. In order to follow the path/line, fuzzy logic techniques are applied to generate movement with the information extracted from vision system. The control method in the system combines a steering controller, which is implemented by an intelligent hybrid fuzzy PID controller, controlling the steering angle of the robot, and fuzzy velocity controller, which controls the forward linear velocity with the purpose of safe tracking. We do experiments using an open source differential-drive wheeled robot platform (Figure 1). In the experiments, the robot is successfully able to follow a pre-defined path/line containing both straight and curved sections. The effectiveness of the control algorithm, combining steering and velocity controllers, is examined in these experiments. Keywords: Control systems, vision system, fuzzy logic, steering controller, velocity controller. List of Symbols

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Section: Figure 8 the Fuzzy Steering Controllermentioning

confidence: 99%

Vision Based Autonomous Path/Line Following of a Mobile Robot Using a Hybrid Fuzzy PID Controller

Thuy¹,

Cường²

2016

Tuyển Tập Công Trình HNKH Toàn Quốc Lần Thứ 3 Về Điều Khiển &Amp; Tự Động Hoá VCCA - 2015

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“…A data-driven rule base learning mechanism intuitively extracts rules from raw data to generate a rule base, which are in the format of antecedents associated with a corresponding consequent (Wang and Mendel 1992;Hong and Lee 1996). Rule base generation can also follow an iterative procedure (Hoffmann 2004;Galea and Shen 2006) to incrementally add new rules to the rule base.…”

Section: Iterative Rule Base Generationmentioning

confidence: 99%

Improving fuzzy rule interpolation performance with information gain-guided antecedent weighting

Shang

et al. 2017

Soft Comput

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Fuzzy rule interpolation (FRI) makes inference possible when dealing with a sparse and imprecise rule base. However, the rule antecedents are commonly assumed to be of equal significance in most FRI approaches in the implementation of interpolation. This may lead to a poor performance of interpolative reasoning due to inaccurate or incorrect interpolated results. In order to improve the accuracy by minimising the disadvantage of the equal significance assumption, this paper presents a novel inference system where an information gain (IG)-guided fuzzy rule interpolation method is embedded. In particular, the rule antecedents in FRI are weighted using IG to evaluate the relative importance given the consequent for decision making. The computation of antecedent weights is enabled by introducing an innovative reverse engineering process that artificially converts fuzzy rules into training samples. The antecedent weighting scheme is integrated with scale and move transformation-based interpolation (though other FRI techniques may be improved in the same manner). An illus-

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“…These systems can, therefore, effectively translate qualitative knowledge into numerical reasoning [10] and are primarily concerned with quantifying and reasoning using natural languages in which words have ambiguous meanings [4]. A common term, computing with words, that has been introduced by Zadeh [11] is now often used to explain the notion of reasoning linguistically rather than quantitatively [10].…”

Section: Introductionmentioning

confidence: 99%

“…These include manufacturing [2], [4], [8], [13], reliability analysis [10], economics [2]- [4], [8] and even medical diagnosis [10], [14], [15]. Fuzzy logic has also been used in several petroleum-engineering-related applications including petrophysics [16], [17], reservoir characterization [18], enhanced oil recovery [19], infill drilling [20], decision-making analysis [21], and well simulation [22], [23].…”

Section: Introductionmentioning

confidence: 99%

“…An expert system can also be designed to solve problems with incomplete data and/or non-exact solutions through the use of fuzzy logic in the inference engine [1]. Fuzzy rule-based expert systems contain such fuzzy rules in their knowledge base and derive conclusions from user inputs and through a fuzzy reasoning process [2]- [4]. These systems have been proved to be effective tools for representing vague knowledge [5]- [7] and are now widely used to perform troubleshooting and control operations because of these similarities to human reasoning as well as their simplicity [2]- [4], [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Expert System for Converting Process Flow Diagrams to Piping and Instrumentation Diagrams

Baloochy¹,

Bozorgmehry²

2010

IJCEA

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Abstract-P&IDs (Piping and instrumentations diagrams) are detailed schematic diagrams of process plants. The production of P&IDs is tedious; involving continual checks of correct connectivity, correct equipment selection, etc. A fuzzy expert system has been developed to aid in PFD to P&ID conversion. The system has been constructed using a set of fuzzy rules based upon individual unit operations. It consists of two main parts: database and application program. The database has been designed using Rational Rose software, and it has been implemented using InterBase. The application program has been designed base on object oriented paradigm and UML standard, and then it has been implemented using Borland C++ Builder compiler. Performance of the developed software has been evaluated base Tennessee Eastman benchmark. This case study shows that the software is capable of handling the PFD to P&ID conversion providing a rich knowledge base.

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Induction of fuzzy rules and membership functions from training examples

Cited by 345 publications

References 11 publications

Vision Based Autonomous Path/Line Following of a Mobile Robot Using a Hybrid Fuzzy PID Controller

Vision Based Autonomous Path/Line Following of a Mobile Robot Using a Hybrid Fuzzy PID Controller

Improving fuzzy rule interpolation performance with information gain-guided antecedent weighting

A Fuzzy Expert System for Converting Process Flow Diagrams to Piping and Instrumentation Diagrams

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