Journal of Intelligent & Fuzzy Systems - Volume 33, issue 5

Authors: Li, Zhen-Guo | Ji, Ze-Sheng | Jiang, Li-Li | Yu, Si-Wen

Article Type: Research Article

Abstract: In this paper, in order to determine effects of citric acid and phosphoric acid on properties of magnesium oxysulfate (MOS) cement, different amount of the two additives were added to MOS specimens with constant molar ratio of MgO: MgSO4: H2O. And the strength development, phase composition and microstructure were tested. The results show that 0.1wt% citric acid and phosphoric acid added into the cement will be increasing the strength and get ideal microstructure. However, for the same amount of citric acid and phosphoric acid, compressive strength of the MOS paste are higher modified by citric acid than phosphoric acid. The …results from XRD indicate that the specimens modified by citric acid and phosphoric acid yield the same hydration products in MOS. As compared to the mixture with phosphoric acid, the microstructure of the mixture with citric acid is more homogenous, and with interlaced needle shaped crystals. Show more

Keywords: Magnesium oxysulfate cement, additives, compressive strength, phase composition, microstructure

DOI: 10.3233/JIFS-169353

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3021-3025, 2017

Authors: Wang, Hua | Huang, Lu | Ren, Peiyu | Zhao, Rong | Luo, Yuyan

Article Type: Research Article

Abstract: In the social network, trust information features dynamic and incomplete. The experts’ weight information is unknown beforehand, or there is not enough reliable sources. The dynamic process of trust propagation is analyzed, a new TS-UTOWA operator for multipath propagation is introduced, and an innovative D-UTOWA operator is proposed for trust aggregation in this paper. As for the efficiency of uninorm trust propagation, the average value of the other’s values is adopted to estimate the incomplete sociomatrix vector. Finally, a case about tourist route arrangment is discussed to demonstrate the efficiency and feasibility of the theory in this article.

Keywords: Dynamic, incomplete, SN-GDM, trust propagation, trust aggregation

DOI: 10.3233/JIFS-169354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3027-3039, 2017

Authors: Han, Renjie | Cao, Qilin

Article Type: Research Article

Abstract: In this paper, via chance constrained programming formulation and fuzzy membership, we give suggestions on a new fuzzy chance constrained least squares twin support vector machine, which can make data measurement noise efficiently. In this paper, we concentrate on least squares twin support vector machine classification when data distributions are uncertain statistically. The model’s function is used to guarantee the small probability of misclassification for the uncertain data, with some known characters of the distribution. The fuzzy chance constrained least squares twin support vector machine model can be transformed into second-order cone programming (SOCP) through the properties of moment information …of uncertain data and thus the dual problem of SOCP model is introduced. Besides, through the numerical experiments we also demonstrate the model’s performance in real data and artificial data. Show more

Keywords: Support vector machine, robust optimization, chance constraints, uncertain classification

DOI: 10.3233/JIFS-169355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3041-3049, 2017

Authors: Guo, Lin | Hu, Xue-Min | Ye, Bo | Zhang, Yi

Article Type: Research Article

Abstract: A novel kernel regression regularized adaptive sparse (KR-RAS) model is presented in this paper for multi-frame super-resolution (SR) reconstruction, by incorporating KR estimation and the clustering-based dictionary learning into a unified sparse reconstruction framework. The basic idea behind our model is to exploit both the global structural self-similarity throughout all frames as prior constraints, and sparsity constraints, to regularize the ill-posed reconstruction for better estimation. In the proposed method, normalized steering kernels are introduced as features for structural clustering of image patches, to aggregate more structurally similar patches for dictionary learning. Furthermore, KR estimation is extended from local neighborhood to …the global neighborhood that is constituted by similar patches from any position of all frames, so more accurate regression estimation of pixel values is possible. Extensive comparisons of experimental results on real video sequences show that the performance of the proposed method outperforms the state-of-the-art methods both subjectively and objectively in most cases. Show more

Keywords: Super-resolution (SR), kernel regression (KR), clustering, sparse representation, dictionary learning

DOI: 10.3233/JIFS-169356

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3051-3058, 2017

Authors: Yu, Yu

Article Type: Research Article

Abstract: This paper has considered an automobile industry duopoly model with representative firms of kuaiche and taxi. The impact of product differentiation degree, market share of adaptive player and price adjustment speed of bounded rationality player on stability region and Nash equilibrium points of system have been analyzed. Numerical simulation has illustrated that product differentiation has increased the possibility of chaos, but chaos has existed not only in a fierce competitive market but also a weak competitive market. Another finding is that generally speaking, the increase of market share and product differentiation degree has increased equilibrium price of kuaiche and decreased …equilibrium price of taxi. This means cash burning war strategy of kuaiche has worked. We choose different price adjustment speed to show dependence on initials only when the system is in chaos. We find suitable control factors to restrain and eliminate chaos. Show more

Keywords: Kuaiche, product differentiation, chaos, market share

DOI: 10.3233/JIFS-169357

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3059-3067, 2017

Authors: Zhong, Zu-Chang | Pan, Wen-Tsao | Luo, Shi-Hua | Yang, Tian-Tian

Article Type: Research Article

Abstract: With the progress in science and technology, many types of electrical equipment have been invented, making the use of electricity more extensive, and living environment more comfortable. However, in modern times, every country stresses the need to promote green energy in order to reduce environmental damage, while the Taiwanese government made an attempt to adjust electricity price as a means to make Taiwan people to reduce carbon emissions and pollution on the planet. Therefore, the paper takes electricity price on the power consumption of Taiwan people as the research object, observes tariff adjustment trends of relevant government departments, and builds …Taiwan’s average electricity consumption and the average price forecast model to provide references to government and researchers. Firstly, we gather data of electricity consumption and price from Taiwan Power Company’s website, and draw a trend chart to explore the relationship between the two; and respectively work out technical indicators of average electric quantity and electricity prices by referring to stock technical indicators; finally, we compare Neural Network parameters optimized by Grey Fruit Fly Optimization Algorithm (GFOA) to build average power consumption and average electricity price forecasting models, and compare the best prediction model with other three algorithms. The study results demonstrate that the electricity consumption and electricity price trends have different characteristics; it is found out that the prediction model of smoothing parameter σ of General Regression Neural Network optimized by GFOA has better predictive ability compared to prediction models constructed by other three algorithms. Show more

Keywords: Grey Fruit Fly Optimization Algorithm, Artificial Fish Swarm Algorithm, Artificial Bee Colony, General Regression Neural Network, Swarm Intelligence

DOI: 10.3233/JIFS-169358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3069-3077, 2017

Authors: Yuan, Yuan | Liming, Zhang | Rui, Zhou | Du, Siyuan | Yang, Jirui

Article Type: Research Article

Abstract: Innovation is the effective weapon for enterprises to face complicated dynamic environment, and to obtain long-term competitive advantages. Organization innovative climate can promote enterprises’ innovative capability and performance through affecting individual staff attitude, motivation and innovative activities. Therefore, by evaluating organization innovative climate of enterprises with scientific methods, weak points can be discovered and adjusted, so promotion of organization innovative climate is the essence of maintaining enterprises’ innovative energy. Based on domestic and overseas scholars’ research, this paper illustrates new quantitative evaluation method: enterprises’ organization innovative climate on the basis of intuitionistic fuzzy number. Indicators are scored through newly constructed …evaluation indicator system. Scores of enterprises’ organization innovative climate are obtained through the calculation of intuitionistic fuzzy number. This model which is more scientific and completed, avoids the awareness of much evaluation information. And it enriched enterprises’ organization innovative climate evaluation theory and methods. At last, the feasibility and practicability of the approach introduced are proved through empirical analysis. Show more

Keywords: Intuitionistic fuzzy number, organizational innovative climate, evaluation

DOI: 10.3233/JIFS-169359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3079-3086, 2017

Authors: Zhu, Dingju | Lian, Zhaotong

Article Type: Research Article

Abstract: This paper proposed parking robot based on fuzzy reasoning and parking big data. The parking difficulty membership degree was computed on the basis of the time spent for looking for parking space at parking lot. The free membership degree at the current time corresponding to the parking difficulty membership degree of the parking lot can be reasoned out based on the parking difficulty membership degree and the free membership degree of the parking lot at each preset time point. The parking robot will reduce the cost needed to predict the free membership degree of the parking lot.

Keywords: Fuzzy reasoning, parking lot, free membership degree, big data

DOI: 10.3233/JIFS-169360

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3087-3094, 2017

Authors: Jiang, Zhong-Zhong | Jiao, Yi-Ru | Sheng, Ying | Chen, Xiaohong

Article Type: Research Article

Abstract: Intelligent Transportation Systems (ITS) are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems. In this paper, a novel route selection problem based on the envisaged driving mode with dynamic signals in ITS is proposed. It belongs to a kind of the shortest path problem of dynamic weight-varying networks, and the arc-weights of the network vary with the arc-chosen, so it cannot be solved by the existing greedy algorithms. According to the characteristics of the proposed problem, firstly, a dynamic programming model for the driving mode on a single path is established. …Secondly, two algorithms for solving the route selection problem based on the former mode are developed. One is a brute-force algorithm based on matrix expansion with the computational complexity of O   (Nt  × n 2 ). The other is an improved adaptive ant colony algorithm with the complexity of O   (Nc  × m  × n 2 ). Finally, the computational experiments show the advantages and disadvantages of the two effective algorithms with numerical examples. Show more

Keywords: Intelligent Transportation Systems, shortest path problem, dynamic weight-varying networks, brute-force algorithm, ant colony algorithm

DOI: 10.3233/JIFS-169361

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3095-3102, 2017

Authors: Martínez-Albaladejo, Francisco J. | Bueno-Crespo, Andrés | Rodríguez-Bermúdez, Germán

Article Type: Research Article

Abstract: EEG signal is considered a dynamical system, difficult and complex to learn. Therefore Brain Computer Interface Systems need to manage specific time variations of the EEG since the extracted feature are non-stationary. This paper presents a study to test Extreme Learning Machine as a suitable classification method for Motor Imagery Brain Computer Interface. In order to take in to account the time course of the signals new descriptors from three widely known Feature Extraction methods (Power Spectral Density, Hjorth parameters and Adaptive AutoregRessive coefficients) have been obtained by three different techniques: central window, averaging features and linking features. Results shows …that these new descriptors have improved the performance of the Extreme Learning Machine with respect classical techniques. Show more

Keywords: Brain Computer Interface, Extreme Learning Machine, Motor Imagery, kernel

DOI: 10.3233/JIFS-169362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3103-3111, 2017