Fuzzy Systems and Knowledge Discovery 

About: Fuzzy Systems and Knowledge Discovery is an academic conference. The conference publishes majorly in the area(s): Fuzzy logic & Fuzzy set. Over the lifetime, 5389 publications have been published by the conference receiving 26133 citations.

Industry 4.0: Towards future industrial opportunities and challenges

Abstract: Industry 4.0 (the fourth industrial revolution) encapsulates future industry development trends to achieve more intelligent manufacturing processes, including reliance on Cyber-Physical Systems (CPS), construction of Cyber-Physical Production Systems (CPPS), and implementation and operation of smart factories. This paper introduces relevant aspects of Industry 4.0 in relation to strategic planning, key technologies, opportunities, and challenges. Strategic planning includes construction of a CPS network, discussion of two major themes which are based on the smart factory and intelligent production, achieving three integrations (horizontal integration, vertical integration and end-to-end integration) and achieving eight plans which consist of the formulation of system standardization, efficient management etc. Finally, it referred to the enlightenment for China's manufacturing industries, to build China's Industry 4.0.

DSC Approach to Robust Adaptive Fuzzy Tracking Control for Strict-Feedback Nonlinear Systems

Abstract: A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems. By employing radial-basis-function neural networks to account for system uncertainties, the proposed scheme is developed by combining ?dynamic surface control? and ?minimal learning parameter? techniques. The key features of the algorithm are that, first, the problem of ?explosion of complexity? inherent in the conventional backstepping method is avoided, second, the number of parameters updated online for each subsystem is reduced to 2, and, third, the possible controller singularity problem in the approximation-based adaptive control schemes with feedback linearization technique is removed. These features result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, it is shown via input-to-state stability theory and small gain approach that all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, three simulation examples are used to demonstrate the effectiveness of the proposed scheme.

On Geometric Aggregation over Interval-Valued Intuitionistic Fuzzy Information

Abstract: The notion of interval-valued intuitionistic fuzzy set (IVIFS) was introduced by Atanassov and Gargov as a generalization of an intuitionistic fuzzy set. The fundamental characteristic of IVIFS is that the values of its membership function and non-membership function are intervals rather than exact numbers. Some operators have been proposed for aggregating intuitionistic fuzzy sets. However, it seems that there is little investigation on aggregation techniques for dealing with interval-valued intuitionistic fuzzy information. In this work, we develop some interval-valued intuitionistic fuzzy geometric operators, such as the interval-valued intuitionistic fuzzy ordered weighted geometric (IIFOWG) operator, and interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator, etc., which are the generalizations of the geometric aggregation operators based on intuitionistic fuzzy sets. Then we apply the developed operators to solve a multiple attribute decision-making problem involving the prioritization of a set of information technology improvement projects.

Survey of Improving K-Nearest-Neighbor for Classification

Abstract: KNN (k-nearest-neighbor) has been widely used as an effective classification model. In this paper, we summarize three main shortcomings confronting KNN and single out three main methods for overcoming its three shortcomings. Keeping to these methods, we try our best to survey some improved algorithms and experimentally tested their effectiveness. Besides, we discuss some directions for future study on KNN.

Improvement of LEACH protocol for WSN

Abstract: In wireless sensor networks, sensor nodes always have a limited power resource. The energy consumed by transferring data from the sensor node to its destination raises as a critical issue in designing reasonable wireless sensor network routing protocols. In this paper we propose a revised cluster routing algorithm named E-LEACH to enhance the hierarchical routing protocol LEACH. In the E-LEACH algorithm, the original way of the selection of the cluster heads is random and the round time for the selection is fixed. In the E-LEACH algorithm, we consider the remnant power of the sensor nodes in order to balance network loads and changes the round time depends on the optimal cluster size. The simulation results show that our proposed protocol increases network lifetime at least by 40% when compared with the LEACH algorithm.