scholarly journals Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5987
Author(s):  
Pier Luigi Gentili
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Quantum Mechanics ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Human Interaction ◽  
Cognitive Domain ◽  
Bayesian Probability ◽  
Total Probability ◽  
Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes’ formula are conceivable as fuzzy sets and Bayes’ inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

scholarly journals Tingkat Kesulitan Dinamis Menggunakan Logika Fuzzy pada Game Musik Tradisional Jawa Tengah

2021 ◽  
Vol 5 (2) ◽  
pp. 56-64
Author(s):  
Kadhana Reya Wisinggya ◽  
Hanny Haryanto ◽  
T. Sutojo ◽  
Edy Mulyanto ◽  
Erlin Dolphina
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Digital Games ◽  
Way Of Thinking ◽  
Traditional Songs ◽  
The Given

The culture in Indonesia is very diverse, one of which is traditional songs. However, knowledge of traditional songs is still small. Digital Games can spread knowledge about traditional songs, one of which is Central Javanese traditional songs. However, the Game that is made still has static difficulties, so the Game cannot follow the player's ability, resulting in the player feeling bored and not wanting to continue the Game. To generate dynamic difficulties, methods in artificial intelligence can be applied to Games, one of which is Fuzzy. So in this study proposed the application of dynamic difficulties using Fuzzy Logic in music Games / Rhythm Games. Fuzzy Logic is built based on mathematical values and represents uncertainty, where this logic imitates the human way of thinking. Fuzzy Logic can convert crisp input values into fuzzy sets by performing fuzzification. After the input value is converted, the input will be entered into the set of rules provided. Each rule produces a different output. After the process is complete, the output value will be converted back to the crisp output value. Based on the research conducted, it is found that Fuzzy Logic can be applied to music Games where the Game can follow the player's ability based on the given rules.

scholarly journals Issues of Estimating the Quality of the Facilities Management of the Emercom of Russia at the Elimination of the Emergency Situations Consequences

2018 ◽  
Vol 7 (4.38) ◽  
pp. 704
Author(s):  
Valeriy Borisovich Vilkov ◽  
Andrey Kliment’evich Chernykh ◽  
Alexander Alekseevich Tarantsev ◽  
Yuri Evgenievich Aktersky ◽  
Ilya Danilovich Cheshko
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Facilities Management ◽  
Inference Algorithm ◽  
Emergency Situations ◽  
The Russian Federation ◽  
Civil Defence ◽  
Sets Theory

The article deals with the problem of multiobjective optimization with regard to the decision making on the use of the forces and facilities of the EMERCOM of Russia (Ministry of the Russian Federation for Affairs for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters). The purpose of the article is to create a method for prompt and reasonable calculations when making a decision on the use of the EMERCOM forces and facilities to eliminate the consequences of emergency situations. The proposed method uses fuzzy sets, fuzzy logic, and the Mamdani fuzzy inference algorithm. The work gives a substantial example illustrating the application of the mentioned theory to solve the problem of choosing the optimal version of the task performed by the facilities of the EMERCOM of Russia. Regarding the novelty, it should be noted that the quality characteristics of the solutions are fuzzy and not unambiguously defined, and therefore allow applying the effective mathematical apparatus of fuzzy sets theory, fuzzy logic and the Mamdani fuzzy inference algorithm in solving this problem. 

Some Recent Defuzzification Methods

2017 ◽  
pp. 31-48 ◽  
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

Some Recent Defuzzification Methods

Fuzzy Systems ◽  
2017 ◽  
pp. 1003-1019
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

MODELING PNEUMATIC MUSCLE ACTUATORS: ARTIFICIAL INTELLIGENCE APPROACH

2010 ◽  
Vol 07 (02) ◽  
pp. 151-164 ◽  
Author(s):  
PRASHANT JAMWAL ◽  
S. Q. XIE ◽  
SHAHID HUSSAIN ◽  
KEAN AW
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Human Interaction ◽  
Time Dependent ◽  
Light Weight ◽  
Inference System ◽  
Pneumatic Muscle ◽  
Dependent Behavior ◽  
Muscle Actuators

Robot human interaction requires use of safe, compliant and light weight actuators. Conventional linear motors and pneumatic cylinders are normally used to actuate robots to assist and augment human motions. Lately it has been realized that these actuators are not suitable and safe for applications involving human actor. Their large weight, size and stiffer design raise concerns. Pneumatic muscle actuators (PMA) on the other hand are very light weight, compact and have inherent compliance which make them potential candidate for applications involving robot human interaction. Taking on the advantages, these actuators are now being experimented for a variety of medical and rehabilitation applications. However they are not very popular due to their highly nonlinear and time dependent behavior which poses control problems. In this paper, an attempt is being made to accurately predict the uncertain and ambiguous characteristics of PMA using Artificial Intelligence (AI). Conventional tools such as analytical and numerical methods can only model a nonlinear system which is time independent. Time varying nonlinear system characteristics can be best modeled using artificial intelligence-based regression models. In this research, Artificial Neural Network (ANN), Mamdani Fuzzy Inference System (FIS) and Takagi-Sugeno (TS)-based fuzzy system are developed after carefully analyzing the time series data obtained from a real system. To achieve higher accuracy from these models, their parameters are tuned. Parameters of ANN are tuned using back propagation algorithm whereas fuzzy parameters are tuned using three different methods, namely, gradient descent method (GD), genetic algorithms (GA) and Modified Genetic Algorithm (MGA). It was found that the TS fuzzy inference system tuned by MGA provides better accuracy and can also model the time dependent behavior of PMA. The proposed TS fuzzy system is found to perform better in terms of accuracy and maximum deviation when compared to the previous approaches in the literature.

scholarly journals Zadeh's Centenary

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Ioan DZITAC
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Soft Computing ◽  
Special Issue ◽  
Introductory Paper ◽  
And Control

This is the introductory paper in a special issue on fuzzy logic dedicated to the centenary of the birth of Lotfi A. Zadeh published by International Journal of Computers Communications & Control (IJCCC). In 1965, Lotfi A. Zadeh published in the journal „Information and Control” the article titled „Fuzzy sets”, which today reaches over 117 thousand citations. The total sum of citations for all his papers is above 253 thousand. Based on the notion of fuzzy sets, fuzzy logic and the concept of soft computing emerged, bringing extremely important implications to the field of Artificial Intelligence (AI). In 2017, I published, whith F.G. Filip and M.J. Manolescu, a 42-page long paper in the IJCCC about the life and masterwork of Lotfi A. Zadeh, from which I will use some information in this material [15].

scholarly journals MODELLING OF TIME, COST AND RISK OF CONSTRUCTION WITH USING FUZZY LOGIC

2021 ◽  
Vol 27 (6) ◽  
pp. 412-426
Author(s):  
Edyta Plebankiewicz ◽  
Krzysztof Zima ◽  
Damian Wieczorek
Keyword(s):  
Risk Factors ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Cost Calculation ◽  
Time Cost ◽  
Investment Process ◽  
The Cost ◽  
Available Information ◽  
Construction Schedule

The paper presents an overview of the literature from recent years devoted to planning the time, costs and risk of a construction investment using fuzzy logic. It also presents three own original models concerning the issue. The first model is used to build a fuzzy construction schedule taking into account fuzzy norms and the number of workers. The costing model uses fuzzy inference from CBR cases. The aim was to increase the accuracy and correctness of the cost calculation performed for the investor in the construction and investment process with a certain degree of vagueness of the available information about materials. In the last of the presented models, fuzzy sets were used to assess the effects of technological and construction (implementation) risk factors. The presented examples prove the usefulness of fuzzy logic in solving problems in construction, where we have incomplete and imprecise information.

scholarly journals Fuzzy Model for Risk Assessment of Machinery Failures

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 525 ◽  
Author(s):  
Dejan V. Petrović ◽  
Miloš Tanasijević ◽  
Saša Stojadinović ◽  
Jelena Ivaz ◽  
Pavle Stojković
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Mining Industry ◽  
Fuzzy Model ◽  
Assessment Model ◽  
Risk Level ◽  
Engineering System ◽  
Risk Assessment Model

The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.

scholarly journals Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

2017 ◽  
Vol 12 (6) ◽  
pp. 748 ◽  
Author(s):  
Ioan Dzitac ◽  
Florin Gheorghe Filip ◽  
Misu-Jan Manolescu
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Soft Computing ◽  
50Th Anniversary ◽  
Fuzzy Sets Theory ◽  
Early Influence ◽  
Fuzzy Logic Theory ◽  
General Aspects ◽  
Sets Theory

In 1965 Lotfi A. Zadeh published "Fuzzy Sets", his pioneering and controversialpaper, that now reaches almost 100,000 citations. All Zadeh’s papers were citedover 185,000 times. Starting from the ideas presented in that paper, Zadeh foundedlater the Fuzzy Logic theory, that proved to have useful applications, from consumerto industrial intelligent products. We are presenting general aspects of Zadeh’s contributionsto the development of Soft Computing(SC) and Artificial Intelligence(AI),and also his important and early influence in the world and in Romania. Severalearly contributions in fuzzy sets theory were published by Romanian scientists, suchas: Grigore C. Moisil (1968), Constantin V. Negoita & Dan A. Ralescu (1974), DanButnariu (1978). In this review we refer the papers published in "From Natural Languageto Soft Computing: New Paradigms in Artificial Intelligence" (2008, Eds.: L.A.Zadeh, D. Tufis, F.G. Filip, I. Dzitac), and also from the two special issues (SI) of theInternational Journal of Computers Communications & Control (IJCCC, founded in2006 by I. Dzitac, F.G. Filip & M.J. Manolescu; L.A. Zadeh joined in 2008 to editorialboard). In these two SI, dedicated to the 90th birthday of Lotfi A. Zadeh (2011), andto the 50th anniversary of "Fuzzy Sets" (2015), were published some papers authoredby scientists from Algeria, Belgium, Canada, Chile, China, Hungary, Greece, Germany,Japan, Lithuania, Mexico, Pakistan, Romania, Saudi Arabia, Serbia, Spain,Taiwan, UK and USA.

scholarly journals The elimination of the consequences of transport emergencies

2021 ◽  
Vol 34 (06) ◽  
pp. 1677-1688
Author(s):  
Valeriy Borisovich Vilkov ◽  
Andrey Klimentevich Chernykh ◽  
Igor Gennadevich Malygin ◽  
Yuriy Dmitrievich Motorygin ◽  
Alexandr Vladimirovich Skripka
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Multicriteria Optimization ◽  
Optimization Problem ◽  
Fuzzy Inference ◽  
Inference Algorithm ◽  
Emergency Situations ◽  
Input Variables ◽  
Civil Defence ◽  
Multicriteria Optimization Problem

The problem of multicriteria optimization in relation to the decisions made about organizing the material and technical support for equipment and personnel of the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM of Russia) in the context of emergency response on transport has been explored in this article. The existing approaches have been indicated, and another approach to building a single generalized criterion by the given partial criteria for the multicriteria optimization problem has been proposed. The verbal statement of the considered problem of multicriteria optimization has been provided. The goal of the study is to develop a method for solving this multicriteria optimization problem using fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm. A substantial example has been provided, illustrating the application of the stated theoretical provisions for solving the problem of choosing the best option for the equipment and personnel of the EMERCOM of Russia to liquidate the consequences of emergency situations on transport. In terms of novelty, it must be noted that the indicator (output variable) and parameters (input variables) of the problem have been defined ambiguously, fuzzily, which allows to use the efficient mathematical tools of the theory of fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm to solve this problem.

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