A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis

Qiye Zhang; Chunwei Wen

doi:10.1007/978-981-32-9682-4_37

A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis

Qiye Zhang^*
, Chunwei Wen

^*Corresponding author for this work

School of Mathematical Sciences

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The fuzzy logic system is an expert system based on fuzzy logic and fuzzy reasoning. It consists of a series of fuzzy rules, which can better represent expert knowledge and effectively treat uncertainty and inaccuracy existing in the field of medical diagnosis. To overcome the rules explosion problem, this paper proposes a novel single-input rule module connected fuzzy logic system, in which the rules adopt single-input antecedents and multi-output consequents with the form of Fuzzy Weighted Linear Function (SIRM-FWLF for short). The updating formulas using the steepest descent algorithm are also deduced to optimize the system parameters. At last, numerical simulations are conducted in the MATLAB environment to compare our SIRM-FWLF model with Mamdani, TSK fuzzy logic systems and fuzzy functional SIRM inference model for medical diagnosis of breast cancer. The simulation results show that the proposed SIRM-FWLF FLS can provide higher diagnostic accuracy and recall rates than other fuzzy logic systems or fuzzy inference models.

Original language	English
Title of host publication	Proceedings of 2019 Chinese Intelligent Systems Conference - Volume I
Editors	Yingmin Jia, Junping Du, Weicun Zhang
Publisher	Springer Verlag
Pages	357-366
Number of pages	10
ISBN (Print)	9789813296817
DOIs	https://doi.org/10.1007/978-981-32-9682-4_37
State	Published - 2020
Event	Chinese Intelligent Systems Conference, CISC 2019 - Haikou, China Duration: 26 Oct 2019 → 27 Oct 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	592
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	Chinese Intelligent Systems Conference, CISC 2019
Country/Territory	China
City	Haikou
Period	26/10/19 → 27/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Fuzzy logic system
Graded mean integration representation
Single-input rule module
Steepest descent algorithm
Triangular fuzzy number

Access to Document

10.1007/978-981-32-9682-4_37

Cite this

Zhang, Q., & Wen, C. (2020). A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis. In Y. Jia, J. Du, & W. Zhang (Eds.), Proceedings of 2019 Chinese Intelligent Systems Conference - Volume I (pp. 357-366). (Lecture Notes in Electrical Engineering; Vol. 592). Springer Verlag. https://doi.org/10.1007/978-981-32-9682-4_37

@inproceedings{b7873f38f070422aa93b916ed478a018,

title = "A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis",

abstract = "The fuzzy logic system is an expert system based on fuzzy logic and fuzzy reasoning. It consists of a series of fuzzy rules, which can better represent expert knowledge and effectively treat uncertainty and inaccuracy existing in the field of medical diagnosis. To overcome the rules explosion problem, this paper proposes a novel single-input rule module connected fuzzy logic system, in which the rules adopt single-input antecedents and multi-output consequents with the form of Fuzzy Weighted Linear Function (SIRM-FWLF for short). The updating formulas using the steepest descent algorithm are also deduced to optimize the system parameters. At last, numerical simulations are conducted in the MATLAB environment to compare our SIRM-FWLF model with Mamdani, TSK fuzzy logic systems and fuzzy functional SIRM inference model for medical diagnosis of breast cancer. The simulation results show that the proposed SIRM-FWLF FLS can provide higher diagnostic accuracy and recall rates than other fuzzy logic systems or fuzzy inference models.",

keywords = "Fuzzy logic system, Graded mean integration representation, Single-input rule module, Steepest descent algorithm, Triangular fuzzy number",

author = "Qiye Zhang and Chunwei Wen",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2019 ; Conference date: 26-10-2019 Through 27-10-2019",

year = "2020",

doi = "10.1007/978-981-32-9682-4\_37",

language = "英语",

isbn = "9789813296817",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "357--366",

editor = "Yingmin Jia and Junping Du and Weicun Zhang",

booktitle = "Proceedings of 2019 Chinese Intelligent Systems Conference - Volume I",

address = "德国",

}

Zhang, Q & Wen, C 2020, A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis. in Y Jia, J Du & W Zhang (eds), Proceedings of 2019 Chinese Intelligent Systems Conference - Volume I. Lecture Notes in Electrical Engineering, vol. 592, Springer Verlag, pp. 357-366, Chinese Intelligent Systems Conference, CISC 2019, Haikou, China, 26/10/19. https://doi.org/10.1007/978-981-32-9682-4_37

A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis. / Zhang, Qiye; Wen, Chunwei.
Proceedings of 2019 Chinese Intelligent Systems Conference - Volume I. ed. / Yingmin Jia; Junping Du; Weicun Zhang. Springer Verlag, 2020. p. 357-366 (Lecture Notes in Electrical Engineering; Vol. 592).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis

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AU - Wen, Chunwei

PY - 2020

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N2 - The fuzzy logic system is an expert system based on fuzzy logic and fuzzy reasoning. It consists of a series of fuzzy rules, which can better represent expert knowledge and effectively treat uncertainty and inaccuracy existing in the field of medical diagnosis. To overcome the rules explosion problem, this paper proposes a novel single-input rule module connected fuzzy logic system, in which the rules adopt single-input antecedents and multi-output consequents with the form of Fuzzy Weighted Linear Function (SIRM-FWLF for short). The updating formulas using the steepest descent algorithm are also deduced to optimize the system parameters. At last, numerical simulations are conducted in the MATLAB environment to compare our SIRM-FWLF model with Mamdani, TSK fuzzy logic systems and fuzzy functional SIRM inference model for medical diagnosis of breast cancer. The simulation results show that the proposed SIRM-FWLF FLS can provide higher diagnostic accuracy and recall rates than other fuzzy logic systems or fuzzy inference models.

AB - The fuzzy logic system is an expert system based on fuzzy logic and fuzzy reasoning. It consists of a series of fuzzy rules, which can better represent expert knowledge and effectively treat uncertainty and inaccuracy existing in the field of medical diagnosis. To overcome the rules explosion problem, this paper proposes a novel single-input rule module connected fuzzy logic system, in which the rules adopt single-input antecedents and multi-output consequents with the form of Fuzzy Weighted Linear Function (SIRM-FWLF for short). The updating formulas using the steepest descent algorithm are also deduced to optimize the system parameters. At last, numerical simulations are conducted in the MATLAB environment to compare our SIRM-FWLF model with Mamdani, TSK fuzzy logic systems and fuzzy functional SIRM inference model for medical diagnosis of breast cancer. The simulation results show that the proposed SIRM-FWLF FLS can provide higher diagnostic accuracy and recall rates than other fuzzy logic systems or fuzzy inference models.

KW - Fuzzy logic system

KW - Graded mean integration representation

KW - Single-input rule module

KW - Steepest descent algorithm

KW - Triangular fuzzy number

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Y2 - 26 October 2019 through 27 October 2019

ER -

A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis

Abstract

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UN SDGs

Keywords

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