Iranian journal of fuzzy systems
Volume:21 Issue: 1, Jan-Feb 2024

A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.

Fuzzy relational inequalities composed by the min-product operation are established to model the optimal pricing with fixed priority in a single product supply chain system. The solution algorithm has been proposed for solving such an optimization problem and finding the optimal solution (is called lexicographic maximum solution). In this study, a novel approach is proposed to finding the optimal pricing with fixed priority in a single product supply chain system. This approach is based on new properties of solution set in a min-product fuzzy relational inequality. These new properties allow us directly determine the optimal value of variable without many duplicate checks in the solution procedure. A numerical example is provided to illustrate the procedure.

Nowadays the cloud computing environment is widely utilized for transmitting and receiving data securely. Inorder to secure the data the encryption method is used but still due to some limitations the security process is diminished. Therefore, this paper proposes a new algorithm to provide better security while transmitting data through the network. At first, the sensitivity of data is determined using a lightweight convolutional neural network (LWCNN) model which is used to categorize the unclassified data into two categories normal sensitive data and highly sensitive data. After determining the level of data sensitivity, the encryption process is performed further. The efficient hash function-based duplication detection approach is employed to maintain confidential information before outsourcing it to a cloud server. Subsequently, the ideal keys are generated for each data based on its sensitivity level using the proposed fuzzy tuna swarm (FTS) algorithm. Finally, the data is encrypted by converting plain text into ciphertext which is only visible to authorized users. The experimental results show that the LWCNN model utilized for data sensitivity classification achieved 94% accuracy and the FTS algorithm proposed for optimal key generation took much less communication time of about 1800μs than other compared techniques.

As a generalization of nullnorms, semi-t-operators are interesting both in theory and practical applications. In this paper, we investigate some properties of idempotent semi-t-operators on bounded. Furthermore, we propose two construction methods of idempotent semi-t-operators on bounded lattices containing only two different elements which are incomparable with $a$ but comparable with $b$. These methods are generalization of several known ones in the literature.

The alpha-cross-migrativity can be regarded as weaker form of the commuting equation. It has been extensively investigated between some aggregation functions including t-norms, overlap functions, uninorms, and semi-t-operators. Recently, Fang [10] has proposed the alpha-cross-migrativity of t-conorms over fuzzy implications. This paper continues to consider this research topic and mainly focuses on the fuzzy implications generated by additive (resp. multiplicative) generators of continuous Archimedean t-norms and t-conorms. Full characterizations for the alpha-cross migrativity of continuous t-conorms over $(f,g)$-, $k$-, $h$- and $(\theta,t)$-generated implications are obtained. Moreover, some supporting examples for solutions are given.

This article investigates the $H_{\infty}$ control problem for discrete-time interval type-2 (IT2) fuzzy systems with infinite distributed delay via an adaptive event-triggered scheme. The IT2 T-S fuzzy system, which is a development over the (type-1) T-S fuzzy system, has greater effectiveness for the expression of system uncertainty, which will improve the difficulty of analysis. Our main goal is to make more efficient use of network resources by developing an adaptive event-triggered controller for interval type-2 fuzzy systems. In contrast to the traditional triggering method, an adaptive event-triggered technique is proposed to improve bandwidth consumption and network control performance. The triggering function's parameters are based on an adaptive law. Moreover, employing the Lyapunov functional method, the resultant criterion gives sufficient conditions to guarantee that discrete time IT2 fuzzy systems are mean-square exponentially stable with a $H_{\infty}$ performance. Finally, a single-link robot arm model and a DC motor are employed to show the usefulness and efficiency of the obtained theoretical results.

In this article, Multitrial vector-based differential evolution algorithm (MTDE) is proposed as energy and cost management controller under Time of Use (TOU) tariff in grid associated domestic PV-wind power system tied with battery storages. To enrich the energy efficiency of a proposed power system, two optimisation algorithms are proposed in the scheduling operation. TOU billing is a cost- reflective power pricing strategy that has been found as an effective way to reduce peak energy consumption in the residential segment everywhere the world, mainly in industrialised nations. In the optimisation maximizing the cost benefit of the household energy is taken as the objective and the dispatching ratio of electricity sold to the grid and used locally is treated as the optimisation variable. Using MATLAB, the performance of proposed MTDE in the aspect of daily cost benefit and revenue growth rate are presented with the comparative analysis of gravitational search algorithm and conventional self-made for self-consumed and rest for sale (SFC&RFS) mode -based energy management controller. In comparison to the SFC&RFS mode, the GSA-based cost optimization offers a 21.46% increase in revenue growth, while it is improved to 38.7% using proposed MTDE algorithm. The paper also addresses the significance of fuzzy logic based maximum power point tracking (MPPT) of solar PV in enhancing the energy management of the proposed system. The prototype of fuzzy logic MPPT for 10 W solar panel is presented and the tracked maximum power is visualized using Thingspeak IoT cloud server. The tracking speed of the MPPT can be increased by introducing machine learning algorithms at the cost of memory and complexity. In this article, the linear regression-based machine learning algorithm is implemented in the hardware prototype by utilizing the dataset aggregated from fuzzy MPPT and hence the proposed MPPT inherits the characteristics of the fuzzy MPPT with increased tracking speed. Around 75 % data are used for training, 25% of data are used to test the model and it is observed that the root mean square error (rmse) is 5.1334 and mean square error is 26.3521 and the model is utilized as MPPT for the real-time inputs.

In this paper, the class of S-generalized distances such that the involved t-conorms S are ordinal sums is discussed. It is shown that these S-generalized distances can be thought of as families of generalized distances with respect to some Archimedean t-conorms. We also deal with the S-generalized distance aggregations, which merge a family of S_{i}-generalized distances into a new S-generalized distance

Modelling phenomena with interval differential equations (IDEs) is an effective way to consider the uncertainties that are unavoidable when collecting data. Similarly to the theory of ordinary differential equations, IDEs have been parallelly investigated with the interval difference equations from the beginning. These two branches can be regarded as one when unifying continuous and discrete solution domains. A conspicuous advantage when merging these areas is that the proof of several analogous properties in both theories need not be repeated. The paper provides a common and efficient tool for studying IDEs not only with continuous or discrete solution domains but also with more general ones. We propose the diamond-$\alpha$ derivative for interval-valued functions (IVFs) on time scales with respect to the generalized Hukuhara difference. Differently from most of the studies on the derivatives of functions on time scales, using the language of epsilon-delta, the novel concept is naturally studied according to the limit of IVFs on time scales as in classical mathematics. A particular class of IDEs on time scales is then considered with respect to the diamond-$\alpha$ derivative. Numerical problems are elaborated to illustrate the necessity and efficiency of the latter.

The concept of Z-number was introduced by Zadeh in order to deal with partial reliability of information. This concept describes a fusion of fuzzy and probabilistic types of uncertainty. In turn, one of the main fields of dealing with imperfect information is approximate reasoning. For the case of pure fuzzy information this field is well-developed. In contrast, existing studies on reasoning with Z-valued “if-then” rules are scarce. One of the main reasons is high analytical and computational complexity. In this work, we develop an approach to reasoning with such kind of rules. The original approach proposed here allows to deal with sparse rule base and is characterized by relatively low computational complexity. The new concept of similarity of Z-numbers based on Jaccard similarity index and measure of divergence of probability distributions is introduced. Based on similarity degrees of current input Z-numbers and Z-numbers located in rule antecedents, weights of linear combination of Z-numbers in rule consequents are determined. The linear combination is based on operations with Z-numbers proposed by authors. Applications of the proposed approach to evaluation of economic development level for a country and control problem are considered. 

Although a variety of methods have already been developed to convert and adapt a fuzzy automaton to its related language equivalent fuzzy deterministic finite automaton, they can still be applied merely for fuzzy automata which have been characterized over particular underlying sets of truth values. Filling this gap, thus, this study attempts to focus on developing a method for computing a minimal deterministic LB-valued general fuzzy automaton for an LB-valued GFA defined over a locally finite and divisible residuated lattice. This proposed method uses the concept of a reduction graph that helps us achieve a minimal deterministic LB-valued GFA. Accordingly, the present investigation aimed at establishing the notions related to L-valued language identified by LB-valued general fuzzy automata (LBvalued GFA) and also crisp deterministic LB-valued GFA ˜ Fc equivalent to LB-valued GFA ˜ F. It then indicated the properties of ˜ Fc. The method of determinization through factorization of L-valued states and also a method concerning state reduction were proposed and studied in details. In particular, the main focus and contribution of this study was the automaton H( ˜ Fc) which is recognized as a deterministic LB-valued GFA that assures the necessary conditions intended for minimality and that its size is always equal or lesser than a minimal crisp deterministic LB-valued GFA equivalent to that. The related concepts and the results obtained in this study have also been clarified and explicated through representative examples.