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An Intelligent Medical Expert System Using Temporal fuzzy rules and Neural Classifier

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Intelligent Automation & Soft Computing 2023年第1期35卷 1053-1067页

作者： Praveen Talari A.Suresh M.G.Kavitha Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai600062India AMET University Chennai600062India University College of Engineering Pattukkottai600062India

As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the *** it is inferred that diabetes is rampant across the world with the majority of the world population being affected by *** the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to *** avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level *** levels are further refined by using the fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification *** main objective of this proposed model is to detect the diabetes level based on the ***,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes *** proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.

关键词： Diabetes type-1 type-2 feature selection classification fuzzy rules fuzzy cognitive maps classifier

fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

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Journal of Central South University 2010年第1期17卷 123-128页

作者：喻寿益邝溯琼 School of Information Science and Engineering Central South University

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.

关键词： adaptive genetic algorithm fuzzy rules auto-regulating crossover probability adjustment

Support Vector Machine-based fuzzy rules Acquisition System

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Journal of Shanghai Jiaotong university(Science) 2009年第5期14卷 555-561页

作者：黄细霞石繁槐顾伟陈善本 Marine Technology and Control Engineering Key Laboratory of the Ministry of Communications Shanghai Maritime University School of Material Science and Engineering Shanghai Jiaotong University

This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data *** construct the fuzzy inference system using fuzzy basis function(FBF) .The gradient technique is used to tune the fuzzy rules and the inference *** analysis and comparative tests are performed comparing with other fuzzy *** results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility.

关键词： modeling fuzzy rules support vector machine (SVM)

Jitter Compensation Using fuzzy rules and Kalman Filter for Mobile Robots Vision

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Jitter Compensation Using Fuzzy Rules and Kalman Filter for ...

New Approaches to the Prognosis and Diagnosis of Breast Cancer Using fuzzy Expert Systems

2011 Chinese Control and Decision Conference（CCDC）

作者： B.R.Wang is with College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,China.Y.Xu is with College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,China.Y.L.Jin is with College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,China.

Stable vision is the key for docking operation of mobile *** on affine transformation image kinematics and recurrence relations,the sub-region KLT feature extract was designed,and fuzzy rules was established to get potential feature *** of sum of absolute difference was used to match feature *** search template was adapted to make matching fast,and adaptive varying template size scheme was proposed to solve the problem the minimum SAD is not *** solving over-determined image kinematics equations using the least squares algorithm,got the motion *** to the observation model of intended motion,unintended motion was eliminated by Kalman *** using filtered parameters to reconstruct image and designing bilinear interpolation algorithm to deal with non-integer pixel coordinate produced by flaot *** the autonomous robot experiment was *** method proposed meet requirements of accuracy and real time.

关键词： Feature Matching fuzzy rules Kalman Filter Vision Compensation

来源： cnki会议评论

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Journal of Computer and Communications 2024年第5期12卷 151-169页

作者： Elias Ayinbila Apasiya Abdul-Mumin Salifu Peter Awon-Natemi Agbedemnab Department of Computer Science School of Computing and Information Sciences C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.

关键词： EMFES Breast Cancer Type-2 Fl Soft Computing Membership Functions fuzzy Set fuzzy rules Risk Factors.

fuzzy-HLSTM(Hierarchical Long Short-Term Memory)for Agricultural Based Information Mining

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Computers, Materials & Continua 2023年第2期74卷 2397-2413页

作者： Ahmed Abdu Alattab Mohammed Eid Ibrahim Reyazur Rashid Irshad Anwar Ali Yahya Amin A.Al-Awady Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajranSaudi Arabia Department of Computer Science College of Computer Science&Information SystemsNajran UniversityNajranSaudi Arabia Computer Skills Department Deanship of Preparatory YearNajran UniversityNajranSaudi Arabia

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop *** case-based reasoning systems,case representation is critical,and thus,researchers have thoroughly investigated textual,attribute-value pair,and ontological *** big databases result in slow case retrieval,this research suggests a fast case retrieval strategy based on an associated representation,so that,cases are interrelated in both either similar or dissimilar *** soon as a new case is recorded,it is compared to prior data to find a relative *** proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional *** Long Short-Term Memory(HLSTM)is used to evaluate the efficiency,similarity of the models,and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the *** on the results,the proposed approaches allows for rapid case retrieval with high accuracy.

关键词： Machine learning agriculture IoT HLSTM fuzzy rules

fuzzy Mapping Network Using Hierarchical Genetically Learning rules

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The Journal of China Universities of Posts and Telecommunications 1994年第1期1卷 22-28页

作者： WuMeng HeZhenya 等 DepartmentofRadioEngineeting NanJingUniversityofPostsandTelecommunicationsNanJing210003P.R.China De

This paper presents a novel architecture for the approximate reasoning-based fuzzy Adap- tive Mapping Network (FAMN). The FAMN includes two components: (a)fuzzy inference Network,which is composed of a three-layer network according to the structure of employed rules; (b) rules Learning Adapter, witch is used to adjush the membership function of rules forth the serial genetic algorithm. In particular, unlike other adaptive learning methods, learning is achieved by incor- porating the idea of multiple resolution. The tuning is first implemented forth few rules at the coarsest resolution of input/output variables, then with many rules at the higher resolution until the training precise required is obtained. The 2-D sine function net is constructed as an illustrative example. The remits have shown the proposed learning algorithm has better performance.

关键词： fuzzy rules Neural network Genetic Algorithm Function approximation

Modeling and applying credible interval intuitionistic fuzzy reciprocal preference relations in group decision making

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Journal of Systems Engineering and Electronics 2017年第2期28卷 301-314页

作者： Wei Zhou Zeshui Xu Business School Sichuan University International Business School Yunnan University of Finance and Economics

Intuitionistic fuzzy preference relations are powerful techniques used to express uncertain preference information. However, simultaneously providing the exact priority and non-priority intensities could be difficult in real applications. A credible interval intuitionistic fuzzy number (CIIFN) is introduced and a credible interval intuitionistic fuzzy reciprocal preference relation (CIIFRPR) is developed to solve this issue. Unlike intuitionistic fuzzy preference relations, the new preference relations use the CIIFNs to express the preference information such that the decision makers simply provide the priority intensity with interval-valued numbers and calculate the non-preference intensity with the transformed method, which avoids a complex evaluation of non-priority information. Furthermore, some basic operations and comparison laws are investigated, based on which three credible interval intuitionistic fuzzy aggregation operators are proposed. Two models are presented to manage the group decision-making. Finally, a practical case is used to demonstrate the feasibility and reasonability of the proposed preference relations and aggregation operators. © 2017 Beijing Institute of Aerospace Information.

关键词： fuzzy rules fuzzy sets Mathematical operators

Precision control of inverter welding power sources by using T-S fuzzy systems

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China Welding 2007年第4期16卷 72-76页

作者：周漪清黄石生张红兵王振民解生冕 School of Mechanical Engineering South China University of Technology

The functional relationship of approximation accuracy and number of fuzzy sets is used to find the rational balance point between the control accuracy and the control cost of fuzzy systems. This approach efficiently eliminates the drawback of rapid control cost increase caused by blind increase of fuzzy set number in practical engineering. The sufficient conditions for TS fuzzy systems as universal approximators are derived. A special T-S fuzzy system that satisfied these conditions is analyzed, and the simulation results show that when the number of fuzzy sets is increased moderately, the model parameters＇ training epochs can be effectually decreased while the model accuracy improved significantly. A practical welding power source controlled by a T-S fuzzy system is developed with satisfactory experimental results.

关键词： welding fuzzy system nonlinear system approximators fuzzy rules