Lotfi A. Zadeh: On the man and his work

AbstractZadeh is one of the most impressive thinkers of the current time. An engineer by formation, although the range of his scientific interests is very broad, this paper only refers to his work towards reaching computation, mimicking ordinary reasoning, expressed in natural language, namely, with the introduction of fuzzy sets, fuzzy logic, and soft computing, as well as more recently, computing with words and perceptions

Fuzzy Transfer Pricing World: On the Analysis of Transfer Pricing with Fuzzy Logic Techniques

The arm’s length analysis of international transfer prices of multinational firms lacks sound methodological approach of the so-called function and risk analysis. In practice, such analyses are descriptive. Derived from Zadeh’s mathematical theory of fuzzy sets, this paper investigates a quantitative approach to identify the function and risk pattern of related parties of multinational companies. We illustrate our fuzzy logic approach with a simple case.

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

In 1965 Lotfi A. Zadeh published "Fuzzy Sets", his pioneering and controversial paper, that now reaches almost 100,000 citations. All Zadeh’s papers were cited over 185,000 times. Starting from the ideas presented in that paper, Zadeh founded later the Fuzzy Logic theory, that proved to have useful applications, from consumer to industrial intelligent products. We are presenting general aspects of Zadeh’s contributions to the development of Soft Computing(SC) and Artificial Intelligence(AI), and also his important and early influence in the world and in Romania. Several early contributions in fuzzy sets theory were published by Romanian scientists, such as: Grigore C. Moisil (1968), Constantin V. Negoita & Dan A. Ralescu (1974), Dan Butnariu (1978). In this review we refer the papers published in "From Natural Language to 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 the International Journal of Computers Communications & Control (IJCCC, founded in 2006 by I. Dzitac, F.G. Filip & M.J. Manolescu; L.A. Zadeh joined in 2008 to editorial board). In these two SI, dedicated to the 90th birthday of Lotfi A. Zadeh (2011), and to the 50th anniversary of "Fuzzy Sets" (2015), were published some papers authored by scientists from Algeria, Belgium, Canada, Chile, China, Hungary, Greece, Germany, Japan, Lithuania, Mexico, Pakistan, Romania, Saudi Arabia, Serbia, Spain, Taiwan, UK and USA

Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller

Will the artificial intelligence help us?

Автор: Володимир Куклін, д.ф.-м.н., проф., завідувач кафедри, Харківський національний університет імені В.Н. Каразіна, Харків, Україна. E-mail: [email protected] Рецензент: Олександр Кузнецов, д.т.н., проф., Харківський національний університет імені В. Н. Каразіна, Харків, Україна. E-mail: [email protected] what can help us (humanity) artificial intelligence. The unification of artificial neural networks and decision-making expert systems based on the logic has discussed. The integration of formed (human) concepts of the system of fuzzy logic and artificial neural networks, allowed us to understand what is happening in the problem-solving process of neural network. The human brain is MEGA processor, therefore, all the efforts of researchers should be focused on the development of MEGA processor systems of new generation. Noted that for the implement intelligent system similar to the human brain, it is necessary to ensure her connection with the outside world and the ability of self-study. Обсуждается чем может нам (человечеству) помочь искусственный интеллект. Рассмотрено важное объединение систем на основе нейронных сетей и экспертных систем на базе математической логики. Объединение сформированных (человеком) понятий системы нечеткой логики с искусственными нейронными сетями позволило понять, что происходит в процессе решения задачи нейронной сетью. Так как человеческий интеллект это мегапроцессорная система, то подчеркнуто, что основные усилия следует направить на создание мегапроцессорных систем новых поколений. Отмечено, что для реализации интеллектуальной системы, аналогичной мозгу человека, необходимо обеспечить ее связь с внешним миром и возможность самообучения. Обговорюється чім може нам (людству) допомогти штучний інтелект. Розглянуто важливе об'єднання систем на основі нейронних мереж і експертних систем на базі математичної логіки. Об'єднання сформованих (людиною) понять системи нечіткої логіки з штучними нейронними мережами дозволило зрозуміти, що відбувається в процесі рішення задачі нейронною мережею. Так як людський інтелект це мегапроцесорна система, то підкреслено, що основні зусилля слід спрямувати на створення мегапроцесорних систем нових поколінь. Відзначено, що для реалізації інтелектуальної системи, що аналогічна мозку людини, необхідно забезпечити її зв'язок із зовнішнім світом і можливість самонавчання