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This article introduces the space of A-linearly correlated fuzzy complex numbers. Using this space, we study the stationary Schrödinger equation with boundary conditions are given by fuzzy complex numbers. This equation plays an special role in Quantum Mechanics describing the state of the system. We apply the formalism to the step potential, generating quantum results consistent with traditional quantum results. [ABSTRACT FROM AUTHOR]

It plays an important role in the calculation of fuzzy numbers using some special fuzzy numbers to approximate general fuzzy numbers in fuzzy analysis, similar to fuzzy complex analysis. A method is proposed to seek an approximation operator that produces a special kind of fuzzy complex number which is the nearest one to a given fuzzy complex number in this paper. The concept of prismoid fuzzy complex number is introduced, and the nearest prismoid fuzzy complex number of fuzzy complex number which preserve the centroid of the core for the fuzzy complex number with respect to the weighted pseudometric are discussed. Finally, some properties of the approximation operator including scale invariance, identity and nearness are presented. [ABSTRACT FROM AUTHOR]

The goal of this paper is to define for the first time the concept of symbolic 2-plithogenic weak fuzzy complex number as new generalization generated by combining real numbers with symbolic 2-plithogenic numbers. We study the elementary properties of this new class such as Invertibility and nilpotency, with many related examples that explain its novelty. [ABSTRACT FROM AUTHOR]

Since the product of complex numbers and rectangular fuzzy complex numbers (RFCN) is not necessarily a RFCN in the former fuzzy complex linear system (FCLS), the scalar multiplication and addition operations of complex numbers and fuzzy complex numbers (FCN) based on a new representation of FCN are proposed. We also introduce a new method for solving FCLS, which can convert FCLS into two distinct linear systems. One is an n × n complex linear system, and the other is an (m n) × (m n) real linear system, where n is the number of unknown variables, and m is the number of substitutional cyclic sets composed of coefficients of FCLS. In particular, using this method to solve one-dimensional fuzzy linear systems, a (2 n) × (2 n) RLS is obtained, which is consistent with Friedman's method. Finally, FCLS based on the RFCN as a special case are also investigated. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a membership function used to form the fuzzy Mandelbric set and investigate the structural effects of additive and multiplicative dynamic noises on it. The newly defined membership function of this fuzzy set and its perturbations is a generalization of the indicator function for the classical Mandelbric set. We present an algorithm for detecting each complex number's fuzzy membership degree. Through the use of the membership degrees of each complex number and experimental mathematics based on the visualizations of a variety of versions by utilizing computer-aided design, we gain a deep foresight for the structure characteristics of the additive and multiplicative perturbed fuzzy Mandelbric sets. Our novel approach allows us to identify the symmetry states of the Mandelbric set and its perturbations by the membership degrees of complex numbers, unlike the existing methods described in the literature. [ABSTRACT FROM AUTHOR]

An extensive variety of human decision making is based on two-sided or bipolar cognitive assessment of a positive side and a negative side. Zhang's bipolar fuzzy set is represented as { ∀ (x , y) | (x , y) ∈ [ - 1 , 0 ] × [ 0 , 1 ] }. The multiagent decision analysis is based on bipolar judgment of a positive side and a negative side. In this paper, we consider the issues posed by bipolar fuzzy linear systems (BFLSs) and bipolar fuzzy complex linear (BFCL) systems of equations with real and complex coefficients. Primarily, we find the solution of the BFLS of equations by the resort into two parts: one is negative (∙) and other is positive (⋆) n × n linear fuzzy systems; to this aim, we use n × n bipolar fuzzy center system and 2 n × 2 n bipolar fuzzy width system. We describe the fundamentals of proposed method with the aid of n = 2 , 5 -dimensional numerical examples, and we get the weak and strong solutions of the system. Subsequently, we describe a technique to solve BFCL systems of equations whose coefficients are real and complex by a pair of positive (⋆) and negative (∙) n × n real and complex crisp linear systems; to this aim, we use bipolar fuzzy complex center and width. Ultimately, we use our proposed technique to solve a current flow circuit which is defined in terms of a BFCL system with complex coefficients, and we get the unknown current in the form of bipolar fuzzy complex number. [ABSTRACT FROM AUTHOR]