A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

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Multi criteria decision making using fuzzy inference system: A case in manufacturing

2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) ◽

10.1109/iccicct.2014.6993158 ◽

2014 ◽

Author(s):

Hari Vasudevan ◽

Naresh C. Deshpande ◽

Ramesh R. Rajguru

Keyword(s):

Decision Making ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Multi Criteria Decision Making ◽

Inference System ◽

System A

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An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

The Scientific World JOURNAL ◽

10.1155/2014/610652 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 6

Author(s):

Ali Safa Sadiq ◽

Norsheila Binti Fisal ◽

Kayhan Zrar Ghafoor ◽

Jaime Lloret

Keyword(s):

Decision Making ◽

Wireless Networks ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Weight Vector ◽

Point Load ◽

Fuzzy Decision Making ◽

Inference System ◽

Handover Decision ◽

Seamless Mobility

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

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A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

Complexity ◽

10.1155/2018/9012720 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 15

Author(s):

Jose M. Gonzalez-Cava ◽

José Antonio Reboso ◽

José Luis Casteleiro-Roca ◽

José Luis Calvo-Rolle ◽

Juan Albino Méndez Pérez

Keyword(s):

Decision Making ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Real Data ◽

Clinical Variable ◽

Decision Making Process ◽

Drug Supply ◽

Inference System ◽

Surgical Interventions ◽

Physiological Signal

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

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A Fuzzy Inference System for Management Control Tools

Mathematics ◽

10.3390/math9172145 ◽

2021 ◽

Vol 9 (17) ◽

pp. 2145

Author(s):

Carolina Nicolas ◽

Javiera Müller ◽

Francisco-Javier Arroyo-Cañada

Keyword(s):

Decision Making ◽

Organizational Performance ◽

Control Systems ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Small And Medium Enterprises ◽

Belief Systems ◽

Management Control ◽

Inference System ◽

Medium Enterprises

Despite the importance of the role of small and medium enterprises (SMEs) in developing and growing economies, little is known regarding the use of management control tools in them. In management control in SMEs, a holistic system needs to be modeled to enable a careful study of how each lever (belief systems, boundary systems, interactive control systems, and diagnostic control systems) affects the organizational performance of SMEs. In this article, a fuzzy logic approach is proposed for the decision-making system in management control in small and medium enterprises. C. Mamdani fuzzy inference system (MFIS) was applied as a decision-making technique to explore the influence of the use of management control tools on the organizational performance of SMEs. Perceptions data analysis is obtained through empirical research.

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PERHITUNGAN TINGKAT KESIAPAN IMPLEMENTASI SMART CITY DALAM PERSPEKTIF SMART GOVERNANCE DENGAN METODE FIS MAMDANI

Dinamik ◽

10.35315/dinamik.v22i1.7104 ◽

2017 ◽

Vol 22 (1) ◽

pp. 39-48

Author(s):

Sri Eniyati ◽

Rina Candra Noor Santi ◽

Retnowati Retnowati ◽

Sri Mulyani ◽

Khristma Martha

Keyword(s):

Decision Making ◽

Research Methods ◽

Social Services ◽

Fuzzy Inference System ◽

Smart City ◽

Fuzzy Inference ◽

Political Strategies ◽

Inference System ◽

Participation In Decision Making ◽

Smart Governance

Smart City adalah skonsep tata kota yang mengoptimalkan teknologi informasi dan digital untuk meningkatkan kesejahteraan dan kebahagiaan masyarakat, serta meningkatkan layanan Pemerintah. Kota Pekalongan sedang berupaya untuk mempersiapkan diri dalam proses implementasi Smart City. Dalam referensi diketahui bahwa salah satu indikator kesiapan implementasi Smart City adalah Smart Governance, yang terdiri atasi empat indikator utama yaitu Participation in decision-making, public and social services, Transparent Governance, political strategies and perspectives. Dari keempat indikator tersebut diperjelas ke dalam indikator operasional yang lebih mudah diukur secara kuantitatif. Oleh sebab itu metode penelitian dipilih mix research methods karena data yang diperoleh dilakukan melalui cara kualitatif dengan wawancara kepada narasumber. Hasil data dikelola dan diolah menggunakan cara kuantitatif. Cara kuantitatif tersebut adalah metode Fuzzy Inference System (FIS) Mamdani. Dari keempat indikator utama diturunkan menjadi 21 variabel input Hasil yang diperoleh adalah tingkat kesiapan Kota Pekalongan dalam mengimplementasikan Smart City dari Perspektif Smart Governance adalah 1,5 (Sedang).

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