Journal of Intelligent & Fuzzy Systems - Volume 35, issue 2

Authors: Lio, Waichon | Liu, Baoding

Article Type: Research Article

Abstract: Regression model is a powerful analytical tool for estimating the relationships between explanatory variables and the response variable. Traditionally, it is often assumed that the data are observed precisely and characterized by crisp values. However, in many cases, those data are collected in an imprecise way and characterized in terms of uncertain variables. In this paper, the residual analysis of uncertain regression models is provided. Furthermore, an approach to obtain the forecast value and the confidence interval of the response variable for the new explanatory variables is given. Finally, a numerical example of the uncertain regression model is documented.

Keywords: Regression analysis, uncertainty theory, uncertain variable, residual, confidence interval

DOI: 10.3233/JIFS-18353

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2573-2583, 2018

Authors: Rubio, José de Jesús | Lughofer, Edwin | Meda-Campaña, Jesús A. | Páramo, Luis Alberto | Novoa, Juan Francisco | Pacheco, Jaime

Article Type: Research Article

Abstract: In this article, an argument Kalman filter is exposed for the fast updating of a neural network. The argument Kalman filter is developed based on the extended Kalman filter, but the recommended scheme has the next two advantages: first, it has less computational complexity because it only employs the Jacobian argument instead of the full Jacobian, second, its gain is ensured to be uniformly stable based on the Lyapunov approach. The commented scheme is applied for the modeling of two Takagi-Sugeno fuzzy models.

Keywords: Argument Kalman filter, modeling, fuzzy models

DOI: 10.3233/JIFS-18425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2585-2596, 2018

Authors: Souza, Paulo Vitor de Campos

Article Type: Research Article

Abstract: This paper presents a learning algorithm for fuzzy neural networks based on unineurons able to generate interpretation provided by the model through fuzzy rules. The learning algorithm is based on ideas from Extreme Learning Machine, to achieve a low time complexity, and pruning method based on F-scores resulting in accurate models using low complexity resources, using only training data in a single step. Experiments considering binary pattern classification are detailed. Results and statistical evaluation suggest the suggested approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability through a process of pruning performed …in simple steps. Show more

Keywords: Fuzzy neural networks, fuzzy systems, F-Scores, pattern classification

DOI: 10.3233/JIFS-18426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2597-2605, 2018

Authors: Jia, Lifen | Yang, Xiangfeng

Article Type: Research Article

Abstract: Uncertain spring vibration equation is a type of uncertain differential equations, whose external force is affected by an uncertain interference. The solution and inverse uncertainty distribution of solution of uncertain spring vibration equation in different cases have been derived, respectively. This paper proves an existence and uniqueness theorem of solution for general uncertain spring vibration equation in different cases under linear growth condition and Lipschitz condition.

Keywords: Spring vibration equation, uncertain differential equation, uncertain process, uncertainty theory

DOI: 10.3233/JIFS-18467

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2607-2617, 2018

Authors: Li, Yuchen | Wen, Meilin | Kang, Rui | Yang, Zaoli

Article Type: Research Article

Abstract: Recently, assembly line balancing problem with uncertain task time gains more and more attention in the literature. Task time uncertainty may overload workstations. Uncertain task time attributes were studied in the frameworks of the learning theory, fuzzy theory, and probability theory. In this paper, we use a new method, which is the uncertainty theory, to model the uncertain task time as the historical task time information is unavailable. We incorporate the uncertainty into the constraints of the line balancing type-1 problem and propose two new optimization models. We also derive some useful theorems related to the optimal solutions. Further, we …develop an algorithm based on the branch and bound remember algorithm to solve the models. Finally, numerical studies are conducted to illustrate our models and to show the efficiency of the proposed algorithm. Show more

Keywords: Assembly line balancing, uncertainty theory, uncertain programming, uncertain task time attribute

DOI: 10.3233/JIFS-18520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2619-2631, 2018