Generalized Fuzzy Logic with twofold fuzzy set: Learning through Neural Net and Application to Business Intelligence

Reddy, Poli Venkata Subba

doi:10.1109/ifuzzy53132.2021.9605090

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…This study uses the Mamdani fuzzy method, and the following steps are carried out for research: a) Carry out secondary data collection activities needed in calculations and analysis. b) Determine the range and membership function of each attribute c) Determination of the consequent function for each implication rule d) Forming fuzzy implication rules by combining each linguistic attribute on each input variable e) Perform defuzzification by calculating the weighted average of all fuzzy implication rules [18,19,38,40] f) Affirmation (defuzzy) is the process of confirmation (defuzzification) using the help of software Matlab R2017a using the facilities provided in the fuzzy toolbox.…”

Section: Methodsmentioning

confidence: 99%

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Prototype of Water Turbidity Measurement With Fuzzy Method using Microcontroller

Siregar¹,

Sinaga²,

Hanafiah³

2022

IOTA

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Water is a source of much-needed living things such as daily needs and transportation routes, and also as a source of energy. Water is also essential as a water quality factor. Good water for treating cold-water ornamental fish with temperatures below 20o Celcius has a maximum water turbidity value of 10 NTU (Nephelometric Turbidity Unit); if the turbidity level is above 10 NTU, the water will be declared cloudy and affect fish health. The object of this research is ornamental aquarium fish with the type of goldfish. The research method used is qualitative. The research flow begins with observing the problem, then designing and simulating the Arduino Uno as a place to process the measuring data. The prototype of this tool aims to show changes in the level of turbidity of water from the value of water turbidity. This prototype uses the fuzzy method to assist the testing process. This study's results for five days showed that 1 out of 5 tests indicated that the aquarium water was cloudy, namely on the fifth day. The results of this study are expected to be implemented in a prototype for measuring water turbidity using the fuzzy method using a microcontroller. The design of this water turbidity measuring instrument is expected to estimate the turbidity of water or liquid correctly, precisely, accurately, with a small error rate, and notify warnings for replacing ornamental fish aquarium water.

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Section: Methodsmentioning

confidence: 99%

“…minµBANYAK [18], µRENDAH[1600] min (0, 0) 0 minµSEDANG [18], µRENDAH [1600] min(1, 0) 0 minµSEDIKIT [18], µRENDAH [1600] min(0,0) 0…”

Section: Min Function Application Min Function Application With Membe...mentioning

confidence: 99%

Prototype of Water Turbidity Measurement With Fuzzy Method using Microcontroller

Siregar¹,

Sinaga²,

Hanafiah³

2022

IOTA

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“…The fuzzy logic algorithm simulates the driver's driving experience by combining physiological perception and action to obtain planning information through look-up ta-bles based on the system's real-time sensor information to achieve path planning. The algorithms conform to human thinking habits and facilitate the conversion of expert knowledge into control signals with good consistency, stability and continuity [18,19]. Fuzzy control is a computer control technique based on natural language control rules and fuzzy logic reasoning.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

Li Song,

Li Song

2023

Journal of Computers

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<p>With the complexity of the robot operating environment increases, there becoming higher demands on the optimal path planning for robots. Most of the path planning is performed in known environments and static models. However, there are still challenges for robots to perform path planning in complex unknown or dynamic environments, which will suffer from deadlock problems and obstacle avoidance failures. Reinforcement learning (RL) can help fuzzy algorithm to optimize the strategy. However, the difficulty of designing the rewards in RL makes the algorithm require a large number of samples to learn the strategy, resulting in computational complexity. To solve these problems, a new local path planning based on the improved fuzzy and Q(λ)-learning algorithms is proposed, aiming to plan the shortest path and avoid obstacles. For solving the problems of breaking through and avoiding obstacles, a fuzzy controller is designed. The distance of nearest obstacle in front of the mobile robot and the distance between the obstacles in the two breakout directions are regarded as the two inputs for this controller. And the two fuzzy quantities of the mobile robot’s running angle and the safe step length are outputted. In the path planning, the Q(λ)-learning algorithm are used to optimize the weights of the running angle and the safe step, obtaining a more accurate robot position and speeding up path planning efficiency. Furthermore, to solve the overlap problems among the starting point, end point, and obstacles, a safer running environment is designed considering radiuses of these objects. Besides, the mobile robot breakout scheme and sustainable obstacle avoidance scheme are designed to solve the deadlock problem and “large obstacle” avoidance problem, respectively. Simulation results in the sparse and complex operating environment show that our proposed algorithm can plan a relatively optimal and safe path, improving the success rate of path planning.</p> <p> </p>

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“…It introduces a crucial paradigm of computing with words and furnishes an approach for handling imprecision and information granularity within the realm of soft computing. Fuzzy theory offers a means to represent linguistic constructs such as "many," "low," "large," "dark," "bright," and the like [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Salt and Pepper Noise Removing for Color Images

Jalal

2023

joincs

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Within the field of digital image processing applications, observed images frequently exposed to noise corruption stemming from image acquisition or transmission processes. This noise degradation reduces image quality and yields unfavorable outcomes in subsequent processing stages (e.g., segmentation, pattern recognition, and enhancement). Consequently, the mitigation of noise in images assumes paramount importance in the domain of image processing. This study introduces an algorithm centered around fuzzy logic for removing impulse noise from color images. The efficiency of the proposed algorithms is assessed by comparing their performance against various noise reduction methods. Objective metrics, namely peak signal-to-noise ratio and mean square error, substantiate that the proposed algorithms yield commendable outcomes in noise reduction and the preservation of intricate image details across a wide spectrum of noise densities

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Generalized Fuzzy Logic with twofold fuzzy set: Learning through Neural Net and Application to Business Intelligence

Cited by 8 publications

References 11 publications

Prototype of Water Turbidity Measurement With Fuzzy Method using Microcontroller

Prototype of Water Turbidity Measurement With Fuzzy Method using Microcontroller

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

Fuzzy Salt and Pepper Noise Removing for Color Images

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