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

Authors: Zhu, Linli | Pan, Yu | Farahani, Mohammad Reza | Gao, Wei

Article Type: Research Article

Abstract: In recent years, the ontology problem has gained attention in machine learning and it has many applications in various fields. Ontology similarity computation plays a critical role in practical implementations. In ontology learning setting, one learns a real-valued score function that assigns scores to ontology vertices. Then, the similarity between vertices is weighted in terms of the difference between their corresponding scores. The purpose of this paper is to report a new ontology learning algorithm for ontology similarity measuring and ontology mapping by means of magnitude preserving. The classes of ontology loss function are considered in regularization ontology framework, the …ontology function is supposed to be linear, and the gradient descent implement is presented for getting the optimal ontology function. The result data from our four simulation experiments imply that the new proposed ontology trick has high efficiency and accuracy in biology and plant science with regard to ontology similarity measure, and humanoid robotics and education science with regard to ontology mapping. Show more

Keywords: Ontology, similarity measure, ontology mapping, magnitude preserving

DOI: 10.3233/JIFS-169363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3113-3122, 2017

Authors: Wu, Hualong | Gan, Jianzhou | Zhao, Bo | Gao, Wei

Article Type: Research Article

Abstract: Strong connections between the chemical characteristics of chemical compounds, materials and drugs and their topological structures have been proved by lots of previous research. For instance, they get the connections in melting point and boiling point. The chemical indices from these molecular topological structures turn out to be favorable for chemists, material and medical scientists, when they try to get the relevant chemical reactivity, biological activity and physical features. As a result, the shortage of the experiments can be covered and made up, if the conduct the study of the topological indices on the molecular structures. Meanwhile, the study can …also make contributions by providing the theoretical evidence in chemical, pharmaceutical and material engineering. On the basis of graph analysis and computation derivation, some degree and distance mixing indices of some classes of Harary graphs and coronene polycyclic aromatic hydrocarbons are determined in the paper. Therefore, the study lay a theoretical foundation for the material properties. Show more

Keywords: Intelligent computing, molecular structure, Harary graph, generalized degree distance, coronene polycyclic aromatic hydrocarbon

DOI: 10.3233/JIFS-169364

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3123-3135, 2017

Authors: Liu, Xiaoyong | Zhou, Zhili

Article Type: Research Article

Abstract: Reasonable structure of human resource is of great significance to development of an organization, so accurate prediction of human resource structure is a very important research problem. Adaptive Neuro-Fuzzy Inference System (abbreviated as ANFIS) is a high-efficiency learning model, and its distributed network structure has very effective result in establishing nonlinear model and constructing time series prediction model. However, classical ANFIS has some disadvantages, such as difficult determination of structure and large randomness of training parameter setting. This paper provides a hybrid prediction model of human resource structure by using the algorithm based on fusion of PSO with random weight, …RPSO, and ANFIS, named RPSO-ANFIS. The novel algorithm uses RPSO to train relevant parameters of ANFIS and determine network structure of ANFIS. Empirical results shows that, compared with GA-ANFIS and PSO-ANFIS, RPSO-ANFIS has advantages of rapid learning speed, high prediction accuracy and smaller relative mean error, which indicated RPSO-ANFIS has good practical application value in predicting the structure of human resource. Show more

Keywords: Adaptive neuro-fuzzy inference system, PSO, the structure of human resources, random weight, fuzzy logic

DOI: 10.3233/JIFS-169365

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3137-3143, 2017

Authors: Gong, Shu | Siddiqui, Muhammad Kamran | Luo, Yi | Gao, Wei

Article Type: Research Article

Abstract: In the setting of software definition network (SDN), whether there exists an available transmission plan between any two sites is equal to whether there exists a fractional factor after deleting certain vertices and edges in the corresponding graph. A graph G is called a fractional ID-(a , b , m )-deleted graph if after deleting any independent-set I , the remaining graph G  - I is a fractional (a , b , m )-deleted graph. A fractional (g , f )-factor F of a graph G is called a Hamiltonian fractional (g , f )-factor if F …includes a Hamiltonian cycle. Furthermore, we say that G has a ID-Hamiltonian fractional (g , f )-factor if after deleting any independent set of G the remaining graph of G includes a Hamiltonian fractional (g , f )-factor. In this paper, we first give a binding number condition for a graph to be a fractional ID-(a , b , m )-deleted graph, and then two sufficient conditions for graphs to have ID-Hamiltonian fractional (g , f )-factors are obtained. Show more

Keywords: Fractional factor, software definition network, fractional ID-(a, b, m)-deleted graph, binding number, ID-Hamiltonian fractional (g, f)-factor

DOI: 10.3233/JIFS-169366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3145-3152, 2017

Authors: Gao, Wei | Zhu, Linli | Guo, Yun | Wang, Kaiyun

Article Type: Research Article

Abstract: In order to represent the semantics and concepts better, the ontology, as an efficient model, has penetrated into all research areas of the computer science and information technology. In recent years, the ontology framework has also been applied to biology, chemistry, pharmaceutics, geography, and other fields, and it has attracted much attention from the researchers. Multi-dividing ontology algorithm is one of the popular learning approaches for ontology applications in which the vertex set and sample data are divided into several parts. In this paper, we introduce a new ontology optimization scheming by means of representer theorem and kernel function, and …the method is a kind of linear programming. Four experiments are designed for the application of ontology algorithms in different fields to test the effectiveness by comparing the implement data. Show more

Keywords: Ontology, similarity measure, ontology mapping, kernel function, linear programming

DOI: 10.3233/JIFS-169367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3153-3163, 2017

Authors: Zhang, Yaping | Xu, Dan

Article Type: Research Article

Abstract: Multiresolution technique is one of the most efficient approaches to improve the rendering performance, but its design and implementation for massive meshes are still very difficult. This paper gives a solution to the construction and rendering of multiresolution representation for massive meshes. Our approach provides a dual hierarchy: a cluster hierarchy for coarse-grained selective refinement and a vertex hierarchy for progressive meshes contained in the cluster for fine-grained local refinement. In order to promote the speed of the construction and rendering of multiresolution representation for massive meshes, we implement parallel half edge collapse to build the vertex hierarchy in the …clusters and view-dependent refinement for vertex hierarchy in parallel with GPU. To prevent data transmission delay to stall the rendering, a separate CPU thread is used to prefetch clusters data into main memory. Our construction process is faster than a corresponding sequential implementation and the rendering results are of comparable quality. Show more

Keywords: Multiresolution representation, massive meshes, cluster hierarchy, vertex hierarchy, edge collapse, GPU

DOI: 10.3233/JIFS-169368

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 3165-3172, 2017