Fuzzy Logic and Soft Computing—In Memory of Lotfi A. Zadeh

Dear Colleagues,

In 1965, Lotfi A. Zadeh published “Fuzzy Sets”, his pioneering and controversial paper, which has now reached over 132,000 citations. All of Zadeh’s papers have, together, been cited over 231,000 times (Google Scholar).

Starting from the ideas presented in that paper, Zadeh later founded the Fuzzy Logic Theory, which proved to have useful applications, from consumer goods to industrial intelligent products.

In accordance with Zadeh’s definition, soft computing (SC) consists of computational techniques in computer science, machine learning, and some engineering disciplines to study, model, and analyze very complex realities, for which more traditional methods have been either unusable or inefficient.

SC uses soft techniques, contrasting it with classical artificial intelligence hard computing (HC) techniques, and includes fuzzy logic, neural computing, evolutionary computation, machine learning, and probabilistic reasoning.

HC is bound by a computer science (CS) concept called NP-complete, which means that there is a direct connection between the size of a problem and the amount of resources needed to solve that called the “grand challenge problem”. SC helps to surmount NP-complete problems by using inexact methods to give useful but inexact answers to intractable problems.

SC became a formal CS area of study in the early 1990s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to HC. It should be pointed out that the simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive.

SC techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as Boolean logic, or rely heavily on computer-aided numerical analysis (such as finite element analysis).

SC techniques are intended to complement HC techniques. Unlike HC schemes, which strive for exactness and full truth, SC techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. Inductive reasoning plays a larger role in SC than in HC. SC and HC can be used together in certain fusion techniques.

SC can deal with ambiguous or noisy data and is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for SC is the human mind. Artificial intelligence and computational intelligence based on SC provide the background for the development of smart management systems and decisions in the case of ill-posed problems.

We are pleased to invite you and your collaborators to submit your research papers for the Special Issue, “Fuzzy Logic and Soft Computing”, which is in memory of Lotfi A. Zadeh (1921–2017).

Prof. Dr. Sorin Nadaban
Guest Editor

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• fuzzy mathematics
• soft computing
• fuzzy logic
• fuzzy sets theory
• neuro-fuzzy applications