Journal of Intelligent & Fuzzy Systems - Volume 38, issue 4

Authors: Ghasemzadeh, Mehdi | Hadidi, Khayrollah

Article Type: Research Article

Abstract: In this paper, the design and simulation results of a general-purpose fuzzy logic controller (FLC) with mixed-signal (analog and digital) inputs and digital outputs are presented. Based on a new strategy, it provides simplicity and high speed from the analog prospective and a total digital system advantages with unchanged digital system properties. A novel and reliable structure with respect to other topologies for the fuzzifier section is designed which enhances the accuracy and the velocity. In order to detect minimum and maximum of the input currents at the same time, an inference engine consisting of a min & max circuit …is an addition. The benchmark for the defuzzifier in the proposed design is simplicity and through a simple approach, the center of area (COA) is attributed to the defuzzifier. The proposed controller circuit consists of two inputs, sixteen rules and one output designed in 0.35μ m CMOS standard technology and simulated with MATLAB systematically. The total controller circuit is simulated with HSPICE simulator (BSIM3v3 parameters) and the layouts were extracted with Cadence Virtuoso v 5.1. The inference speed of the controller is about 41.3 MFLIPS (fuzzy logic inference per second) and power consumption is 3.2 mW. Show more

Keywords: A/D converter, CMOS fuzzy controller, current mode circuits, defuzzifier, fuzzifier, fuzzy logic

DOI: 10.3233/JIFS-191672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5033-5044, 2020

Authors: Rajendra Thilahar, C. | Sivaramakrishnan, R.

Article Type: Research Article

Abstract: A tele-operated robot stereo vision system is used for stretching out the operator’s eye-hand motion and its distance based co-ordination with experts. The major challenge is the reduction of communication delay by using effective decisions to avoid tele-operation instability. This problem can be handled effectively by using the principles of Augmented Reality which provides facilities for superimposing virtual objects onto the real video images of the workspace to create a simulation plan in the client system. In this paper, we propose a new feature selection algorithm called Fuzzy Rules and Information Gain Ratio based Feature Selection Algorithm for selecting the …optimal number of features from the full set of available features. Also, a new Fuzzy Rule based Neuro-Genetic Classification Algorithm is proposed in this paper for classifying the augmented images more accurately. The main advantages of the proposed model are reduction in classification and communication time and increase in decision accuracy. Show more

Keywords: Image classification, augmented reality, virtual reality, fuzzy systems, robot motion

DOI: 10.3233/JIFS-191674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5045-5054, 2020

Authors: Qiao, Sha | Zhu, Ping

Article Type: Research Article

Abstract:  A large number of studies have investigated the systems of fuzzy relation equations and inequalities, which have a much wider field of application. In this paper, we study several types of systems of fuzzy relation equations and inequalities consisting of a given family of k -ary fuzzy relations, where natural number k  ≥ 2, and three unknown fuzzy relations over complete residuated lattices and meet-continuous lattices. Their solutions are triples of fuzzy relations. For the systems of fuzzy relation inequalities, we give the greatest solutions contained in a given triple of fuzzy relations, the least solutions containing a given triple of …fuzzy relations, or give maximal solutions contained in or containing a given triple of fuzzy relations, or belonging to a given interval of triples of fuzzy relations over complete residuated lattices and complete meet-continuous lattices. For the systems of fuzzy relation equations, we present a method of computing maximal solutions contained in a given triple of fuzzy relations and a method of computing minimal solutions containing a given triple of fuzzy relations. Furthermore, we provide some conditions under which there exist the greatest solutions contained in and the least solutions containing a given triple of fuzzy relations. Show more

Keywords: Fuzzy relation, fuzzy relation equation, fuzzy relation inequality, complete residuated lattice, complete meet-continuous lattice

DOI: 10.3233/JIFS-191695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5055-5076, 2020

Authors: Lin, Chih-Hong

Article Type: Research Article

Abstract: A six phase copper rotor induction motor (SPCRIM) drive system still exists in lots of nonlinear characteristics such as the added load torque, the Stribeck effect torque, the the cogging torque, the coulomb friction torque and the parameters variations. Due to some uncertainties effects, the using linear controller can not achieve better control performance for the SPCRIM drive system. To obtain better performance, a clever backstepping control system using two adaptive laws and a hitting function is proposed for controlling the SPCRIM drive system. To improve larger chattering phenomenon under uncertainties affects for aforementioned control system, the clever backstepping control …system using two adaptive laws, a revised recurrent fuzzy neural network (RRFNN) and a compensated controller is proposed to estimate the required lumped uncertainty and to compensate the minimum reconstructed error of the estimated law. Furthermore, the corrected particle swarm optimization (CPSO) algorithm by using variable dynamic inertia weight and variable dynamic constriction factor with segment regulation mechanics that is the innovativeness for using the CPSO algorithm is adopted to regulate four variable learning rates of the weights in the RRFNN to speed-up parameter’s convergence. Finally, comparative performances through some experimental results are verified that the clever backstepping control system using two adaptive laws, a RRFNN and a compensated controller has better control performances than those of the proposed methods for the SPCRIM drive system. Show more

Keywords: Backstepping control, copper rotor induction motor, Lyapunov stability, particle swarm optimization, recurrent fuzzy neural network

DOI: 10.3233/JIFS-191712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5077-5093, 2020

Authors: Imran, Muhammad | Siddiqui, Muhammad Kamran | Baig, Abdul Qudair | Shaker, Hani

Article Type: Research Article

Abstract:  The topological descriptor are numerical parameters of a graph which characterize its topology and are usually graph invariants. Nowadays, Biological science is an energizing and quickly creating branch of knowledge together with the topological descriptor. In recent years, the investigation of living things has experienced huge extension. All the living things are composed of a fundamental unit of life called cells. The microbiology is a science that deals with the living creatures that can not be seen by naked eyes like bacteria, viruses. In recent years, eccentricity based topological indices gain a lot of importance in many disciplines like chemistry, …computer science, integrated circuits, electric circuits, communication networks, biological networks. In a connected graph G , the vertex set V  (G ) shows the bacteria and the edge set E  (G ) shows the relationship between two bacterium. Mostly, the reproduction of bacteria and other microorganisms occur by binary fission process. The topological indices play a vital and useful role in indicating and analyzing physical, chemical and biological properties of any molecular graph. In this paper, we have computed eccentric polynomial and eccentric atom bond connectivity index of hyper binary trees networks (k -level) and relate these networks to biological networks. Also discuss how biological activities of these networks work in daily life. Show more

Keywords: Biological networks, molecular structure descriptor, eccentric atom bond connectivity, eccentric connectivity polynomial, binary tree, hyper binary trees networks

DOI: 10.3233/JIFS-191714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5095-5105, 2020

Authors: Akram, Muhammad | Dudek, Wieslaw A. | Habib, Amna | Al-Kenani, Ahmad N.

Article Type: Research Article

Abstract: The imperfect competition models are equipped by fuzzy set theory with direct assessments of uncertainty. An appropriate point of departure for origination of a system with potentially broader coverage can be provided in view of fuzzy sets. In this way, several extensions of fuzzy set have been introduced to deal with uncertain and ambiguous information including relationships between objects. The q-rung picture fuzzy (q-RPF) model, which inherits the virtues of q-rung orthopair fuzzy set and picture fuzzy set, is one of the convenient way to represent such information. In order to exhibit interactions in various economic structures the conception of …q-RPF economic competition graphs can be employed. Thus the intention of present study is to deal with q-rung picture fuzzy competition graphs (q-RPFCGs) and in particular, q-rung picture fuzzy economic competition graphs (q-RPFECGs) with its generalizations: q-RPF k -economic competition graphs; p -economic competition q-RPFGs; and m -step q-RPFECGs through several important results. Furthermore, this paper offers a brief review for perfect and imperfect competition in competitive market structures and sketch q-RPFECGs to represent duopoly, oligopoly, and monopolistic competitions in graph theoretic approach. Also, it designs an algorithm to calculate the strength of economic competition among buyers and sellers in imperfect competitive markets with q-RPF information. Show more

Keywords: q-Rung picture fuzzy competition graphs, q-rung picture fuzzy economic competition graphs, imperfect competition models, duopoly, oligopoly, monopolistic competition

DOI: 10.3233/JIFS-191726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5107-5126, 2020

Authors: Vinh An, Truong | Van Hoa, Ngo

Article Type: Research Article

Abstract: In this work, a new class of generalized fractional integral equations involving the kernel ψ -function in the fuzzy setting is introduced. With this problem, we can recover a wide class of fractional fuzzy integral equations by choosing the kernel ψ -function. In this sense, we provide sufficient conditions for the existence, uniqueness of solutions and δ -Ulam-Hyers-Rassias stability of the given problems. Some examples are given to illustrate our main results.

Keywords: δ-Ulam-Hyers-Rassias, kernel ψ-functions, Fuzzy fractional integral equations

DOI: 10.3233/JIFS-191743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5127-5141, 2020

Authors: Vinh An, Truong | Van Hoa, Ngo

Article Type: Research Article

Abstract:  In this work, we consider a new form of fuzzy fractional Volterra integral equations (FFVIEs) involving the generalized kernel functions. By using the monotone iterative technique (MIT) combined with the method of lower and upper solutions, the existence of extremal solutions of FFVIEs is established. Some examples are given to illustrate our main results.

Keywords: Fuzzy fractional integral equations, generalized kernel functions, extremal solutions, monotone iterative technique

DOI: 10.3233/JIFS-191746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5143-5155, 2020

Authors: Akbari, Reza | Dabbagh, Rahim | Ghoushchi, Saeid Jafarzadeh

Article Type: Research Article

Abstract: One of the most crucial components in risk management in an organization is detection of risk modes in a system, prioritization of them and making plans in order to enact corrective actions. And one of the common methods for prioritization of risks is the conventional Failure Mode Effects Analysis (FMEA). Although this approach is widely used in different industries, it suffers from some shortcomings, which can lead to failures in reaching reality-based results. This research study, therefore, proposed an approach in three phases for the compensation of the shortcomings of the FMEA method. In the first phase, the FMEA method …was used to detect different risk modes and then assign values to the Risk Priority Number (RPN) determinant factors. In the second phase, the weights of the triple factors were calculated by means of Fuzzy Best-Worst Method (FBWM) and experts’ opinions. And finally, with respect to the outputs of previous phases, the risks were ranked by means of the proposed Z-WASPAS method. In addition to the assignment of different weights to the triple factors and considering the feature of uncertainty in these factors, the proposed approach paid attention to reliability in the risk modes via the Z-Numbers theory. The proposed approach was applied in the operation processes of Mes-e Sarcheshmeh molybdenum factory in Iran and the results indicated a full ranking of risks compared to other conventional methods such as FMEA and fuzzy WASPAS. Show more

Keywords: Failure mode effects analysis, HSE, Z-Numbers, fuzzy BWM, WASPAS

DOI: 10.3233/JIFS-191749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5157-5173, 2020

Authors: Shukla, Shilpi | Jain, Madhu

Article Type: Research Article

Abstract: Deep learning is far and wide considered to be the most powerful method in computer vision fields, which has a lot of applications such as image recognition, robot navigation systems, and self-driving cars. Recent developments in neural networks have led to an efficient end-to-end architecture to human activity representation and classification. In light of these recent events in deep learning, there is now much considerable concern about developing less expensive computation and memory-wise methods. This paper presents an optimized end-to-end approach named stochastic deep conviction network (SDCN) formulated using the deep learning method. It comprises of deep learning method namely …deep belief network (DBN), two supervised machine learning algorithm support vector machine (SVM) and decision tree (DT) with optimization capability for speech emotion identification. In the beginning, pre-processing is performed and the features are automatically extracted from the input speech signal by the DBN. Since speech signal features loses most of the information and the performance cannot be guaranteed because dynamic interactions can generate uncountable emotion-specific experiences that have the same core feeling state but different perceptual inclinations so DBN provides more robust features. The next step is to classify the emotions in the training phase; here the SVM classifier is chosen which performs dual classification. In order to enhance this classification process, defects must be reduced and the best discrimination of the extracted features should be obtained hence particle swarm optimization (PSO) technique is being added along with SVM classifier in the training phase. To reduce the over fitting problem and risks of a single classifier a DT is being used in the testing phase for the exact identification of emotions (anger, disgust, fear, happiness, neutral and sadness) and therefore it obtains better performance than a single classifier. The complication of the decision tool is that it can increase the computation time. Thus to eliminate this defect whale optimization (WO) technique is being added to the decision tree to reduce the complexity of the system, which in turn lessens the time taken for recognizing the emotion of the speech signal. This formulated proposed SDCN system improves the recognition rate accurately. In this work, theMATLAB environment is being preferred to perform speech emotion recognition. Using the proposed technique the achieved accuracy of emotion detection is above 95% and the identification of various emotions exceeds 98% recognition rate with a computation time of 23 seconds, which has not been achieved so far by any other existing techniques. Show more

Keywords: Stochastic deep conviction network, restricted Boltzmann machine, particle swarm optimization, support vector machine, whale optimization

DOI: 10.3233/JIFS-191753

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5175-5190, 2020