Journal of Intelligent & Fuzzy Systems - Volume 39, issue 3

Authors: Araújo Júnior, José M. | Linhares, Leandro L.S. | Araújo, Fábio M.U. | Almeida, Otacílio M.

Article Type: Research Article

Abstract: Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wavelet neural network (FWNN) can be regarded as a prominent technique, consisting of …the combination of wavelet neural network (WNN) and adaptive network-based fuzzy inference system (ANFIS). This work proposes the use of FWNN to infer the temperature and humidity values inside the incubator in order to certify the equipment operation. Results obtained with the analyzed neural system have shown the generalization and inference capacities of FWNNs, thus allowing their application to practical tasks aiming to increase the efficiency of incubators. Show more

Keywords: Fuzzy wavelet neural networks, inferential sensors, neonatal incubators, system identification

DOI: 10.3233/JIFS-190129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2567-2579, 2020

Authors: Yang, Yu | Wang, Jian-Qiang | Wang, Jing

Article Type: Research Article

Abstract: In this study, a multi-criteria group decision making (MCGDM) framework is constructed for electric vehicle fast-charging station (EVFCS) selection using a proportional hesitant fuzzy set (PHFS) that can describe two aspects of information: the possible membership degrees in the hesitant fuzzy elements and associated proportion representing statistical information from different groups. A newly extended distance measure for PHFSs is introduced and an extended maximizing deviation method is constructed to obtain criteria weights objectively. Accordingly, an integrated PHFS-VIKOR (VlseKriterijum-ska Optimizacija I Kompromisno Resenje) method embedded with a new distance measure and extended maximizing deviation method is presented. With increasing concerns about …range anxiety, it is essential to seek an optimal location for EVFCS considering efficient utilization of resources and long-term development of socio-economy under proportional hesitant fuzzy environment. Lastly, an illustration with sensitivity analysis and comparative analyses is provided to demonstrate the validity and robustness of our proposal. Show more

Keywords: Multi-criteria group decision making, proportional hesitant fuzzy set, distance measure, VIKOR, maximizing deviation method

DOI: 10.3233/JIFS-190156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2581-2596, 2020

Authors: Kim Son, Nguyen Thi | Long, Hoang Viet

Article Type: Research Article

Abstract:  In this paper, we consider Cauchy problems for second order fuzzy functional differential equations (DEs) with generalized Hukuhara (gH) derivatives. We study the solvability of the problem by using Perov fixed point theorem in ordered partial metric spaces. The data monotony, continuity, diferentiability dependence of mild solutions with respect to parameters are investigated via weak Picard operators. Moreover, the stability of mild solutions is addressed in sense of Ulam-Hyers stability related to the technique of coefficient matrix converges to zero. Some examples are presented to demonstrate for theoretical results.

Keywords: Fuzzy functional DEs, gH-derivatives, ordered partial metric spaces

DOI: 10.3233/JIFS-190222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2597-2610, 2020

Authors: Lee, Pin-Chan | Lo, Tzu-Ping | Sun, Haoqing | Wen, I-Jyh

Article Type: Research Article

Abstract: Structure of convolutional neural network (CNN) applied for image recognition requires large numbers of tuning for designated datasets in practice. It is a time-consuming process to finally come up with a feasible structure for specific requirement. This paper proposes a method based on Taguchi method which can efficiently determine the optimal structure of hyperparameters combination. Five hyperparameters with four levels are defined as control factors and two indicators are chosen to measure the performance of CNN structure. L 16  (45 ) orthogonal array is used to arrange the experiment. S/N ratio and main effect plot are used to identify the …optimal structure (hyperparameter combination) of CNN. The classic case of MNIST is employed to verify the practicability of the proposed method. Results show that the proposed method can identify the optimal CNN structure efficiently and also rank the significance priority of hyperparameters. Show more

Keywords: Convolutional neural network, hyperparameter combination, optimization algorithm, Taguchi method

DOI: 10.3233/JIFS-190275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2611-2625, 2020

Authors: Xian, Sidong | Guo, Hailin | Chai, Jiahui | Wan, Wenhua

Article Type: Research Article

Abstract: Hesitant fuzzy linguistic term set (HFLTS) can handle the qualitative and hesitant information in multiple attribute decision making (MADM) problems which are widely used in various fields. However, the experts’ evaluation of information is not completely reliable in the situation where their own knowledge background is insufficient. In order to deal with deviations due to incomplete reliability of the evaluation, this paper first proposes the interval probability hesitant fuzzy linguistic variable (IPHFLV), which takes the HFLTS as the evaluation part and adds a novel element-reliability of evaluation, thus can describe the different credibility of information evaluation due to the familiarity …of experts with schemes and the differences in knowledge cognition. The operation rules and comparison methods are also illustrated. Particularly, under the inspiration of probability theory, we propose the possibility degree of the IPHFLVs. Then we propose IPHFL-AHP based on the AHP and interval probability hesitant fuzzy linguistic variable. Especially, the general geometric consistency index (G-GCI) based on the unbiased estimator of the variance is presented to measure the consistency and the iterative algorithm is constructed to improve the consistency. We use the possibility degree to calculate the priority vector to acquire the total ranking and introduce the process of IPHFL-AHP. Finally, case study of talent selection is given to illustrate the effectiveness and feasibility of the proposed method. Show more

Keywords: Interval probability hesitant fuzzy linguistic variable, interval probability hesitant fuzzy linguistic analytic hierarchy processe, general geometric consistency index, possibility degreee, reliability

DOI: 10.3233/JIFS-190427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2627-2645, 2020

Authors: Feng, Naidan | Liang, Yongquan

Article Type: Research Article

Abstract:  Aiming at the imprecise and uncertain data and knowledge, this paper proposes a novel prior assumption by the rough set theory. The performance of the classical Bayesian classifier is improved through this study. We applied the operations of approximations to represent the imprecise knowledge accurately, and the concept of approximation quality is first applied in this method. Thus, this paper provides a novel rough set theory based prior probability in classical Bayesian classifier and the corresponding rough set prior Bayesian classifier. And we chose 18 public datasets to evaluate the performance of the proposed model compared with the classical Bayesian …classifier and Bayesian classifier with Dirichlet prior assumption. Sufficient experimental results verified the effectiveness of the proposed method. The mainly impacts of our proposed method are: firstly, it provides a novel methodology which combines the rough set theory with the classical probability theory; secondly, it improves the accuracy of prior assumptions; thirdly, it provides an appropriate prior probability to the classical Bayesian classifier which can improve its performance only by improving the accuracy of prior assumption and without any effect to the likelihood probability; fourthly, the proposed method provides a novel and effective method to deal with the imprecise and uncertain data; last but not least, this methodology can be extended and applied to other concepts of classical probability theory, which providing a novel methodology to the probability theory. Show more

Keywords: Rough set theory, prior assumption, Bayesian classifier, approximation quality, probability theory

DOI: 10.3233/JIFS-190517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2647-2655, 2020

Authors: Pham, Ngoc Thuy

Article Type: Research Article

Abstract: This paper propose a novel Port Controlled Hamiltonian_Backstepping (PCH_BS) control structure with online tuned parameters, in combination with the modified Stator Current Model Reference Adaptive Syatem (SC_MRAS) based on speed and flux estimator using Neural Networks(NN) and sliding mode (SM) for sensorless vector control of the six phase induction motor (SPIM). The control design is based on combination PCH and BS techniques to improve its performance and robustness. The combination of BS_PCH controller with speed estimator can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and external load disturbances, enables very good static and dynamic performance …of the sensorless drive system (perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation) in a wide speed range, and robust for the disturbances of the load, the speed variation and low speed. The proposed sensorless speed control scheme is validated through Matlab-Simulink. The simulation results verify the effectiveness of the proposed control and observer. Show more

Keywords: Neural networks, sensorless vector control, six phase induction motor drive, stator current MRAS based on speed observer, backstepping control, port controlled hamiltonian

DOI: 10.3233/JIFS-190540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2657-2677, 2020

Authors: Rao, G. Madhukar | Ramesh, Dharavath

Article Type: Research Article

Abstract: In a real-time application such as traffic monitoring, it is required to process the enormous amount of data. Traffic prediction is essential for intelligent transportation systems (ITSs), traffic management authorities, and travelers. Traffic prediction has become a challenging task due to various non-linear temporal dynamics at different locations, complicated underlying spatial dependencies, and more extended step forecasting. To accommodate these instances, efficient visualization and data mining techniques are required to predict and analyze the massive amount of traffic big data. This paper presents a deep learning-based parallel convolutional neural network (Parallel-CNN) methodology to predict the traffic conditions of a specific …region. The methodology of deep learning contains multiple processing layers and performs various computational strategies, which is used to learn representations of data with multilevel abstraction. The data has captured from the department of transportation; thus, the size of data is vast, and it can be analyzed to get the behavior of the traffic condition. The purpose of this paper is to monitor traffic behavior, which enables the user to make decisions to build the traffic-free cities. Experimental results show that the proposed methodology outperforms other existing methods such as KNN, CNN, and FIMT-DD. Show more

Keywords: Convolutional neural network, deep learning, traffic data visualization, traffic prediction

DOI: 10.3233/JIFS-190601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2679-2691, 2020

Authors: Farag, Wael

Article Type: Research Article

Abstract: In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT ) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly a pipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the careful fusion of the employed algorithms where some of them work in parallel to strengthen …each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. The particulars of the employed algorithms together with their implementation are described in detail. Additionally, these algorithms and their various integration combinations are tested and their performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark with 87% average precision achieved. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions. Show more

Keywords: Computer vision, self-driving car, autonomous driving, ADAS, vehicle detection, vehicle tracking

DOI: 10.3233/JIFS-190634

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2693-2710, 2020

Authors: Hu, Yangguang | Xiao, Mingqing | Li, Shaoyi | Yang, Yao | Wu, Sijie

Article Type: Research Article

Abstract: Infrared target tracking is increasingly becoming important for various applications in recent years. However, it is still a challenging task as limited information can be obtained from the infrared image. Inspired by the excellent performance of deep tracker, a novel tracker based on MDNet is proposed. As the prior information has great value for target tracking, a modified Back-Propagation network is used for predicting the scale of target during tracking. The result of the prediction is used for generating candidate windows for online learning, which can improve the performance of tracker. To evaluate the proposed tracking algorithm, we performed experiments …on the VOT-TIR2016 and AMCOM infrared data. The experimental results demonstrate that our algorithm provides a 1.94% relative gain in accuracy and 21.4% in robustness on VOT-TIR2016 when compared with MDNet. Show more

Keywords: Artificial intelligence, infrared target tracking, convolutional neural network, scale prediction

DOI: 10.3233/JIFS-190787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2711-2723, 2020