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This paper proposes two kinds of minimum cost models regarding all the individuals and regarding with one particular individual respectively, shows the economic significance of these two models by exploring their dual models based on the primal-dual linear programming theories, and builds the conditions when these two models have the same optimal consensus opinion.
A new approach for Delphi processes including a measure of consensus based on linguistic terms is introduced in this paper. The measure of consensus involves qualitative reasoning techniques and is based on the concept of entropy. In the proposed approach, consensus is reached automatically without the need for neither a moderator nor a final interaction among panelists. In addition, it permits panelists...
A recent study in Science indicated that the confidence of a decision maker played an essential role in group decision making problems. In order to make use of the information of each individual's confidence of the current decision problem, a new hybrid weighted aggregation method to solve a group decision making peoblem is proposed in this paper. Specifically, the hybrid weight of each expert is...
In this study, we present a novel selection process to solve the multiperson decision making (MPDM) problems with different preference representation structures. This selection process is based on the prospect theory, which is one of the most influential psychological behavior theories, and seeks to maximize the satisfactory of all decision makers. Specifically, the individual selection methods associated...
In the context of group decision making with fuzzy preferences, consensus measures are employed to provide feedback and help guide automatic or semi-automatic decision reaching processes. These measures attempt to capture the intuitive notion of how much inputs, individuals or groups agree with one another. Meanwhile, in ecological studies there has been an ongoing research effort to define measures...
The multiplicative consistency (MC) property of interval additive reciprocal preference relations (IARPRs) is explored, and then the consistency index is quantified by the multiplicative consistency estimated IARPR. The MC property is used to measure the level of consistency of the information provided by the experts and also to propose the consistency index induced ordered weighted averaging (CI-IOWA)...
This paper compares two alternative feature data meta-representations using Intervals' Numbers (INs) in the context of the Minimum Distance Classifier (MDC) model. The first IN meta-representation employs one IN per feature vector, whereas the second IN meta-representation employs one IN per feature per class. Comparative classification experiments with the standard minimum distance classifier (MDC)...
An artificial neural network model based on dendritic computation using two lattice metrics is introduced in this paper. A description of the mathematical framework of the proposed model is provided and its corresponding learning algorithm is presented in mathematical pseudocode. Computational experiments are given to demonstrate the effectiveness and performance of the learning algorithm as well...
In this paper, we present a novel lattice-based memory model called max-plus projection autoassociative morphological memory (max-plus PAMM). The max-plus PAMM yields the largest max-plus combination of the stored patterns which is less than or equal to the input. Such as the original autoassociative morphological memories (AMMs), it is idempotent and it gives perfect recall of undistorted patterns...
Recent work has proposed an enhancement of Formal Concept Analysis (FCA) in a tunable, hybrid formal context including both numerical and nominal data [1]. This work introduces FCknn, that is a granular knn classifier based on hybrid concepts, whose effectiveness is demonstrated on benchmark datasets from the literature including both numerical and nominal data. Preliminary experimental results compare...
Currently there is a lot effort to define neurological biomarkers from resting state fMRI data for different neurological diseases. fMRI voxels are high dimensional vectors, so that dimensional reduction, to scalar values if possible, is highly desirable. At the same time, biomarkers are to be provided as brain localizations which may have an anatomical interpretation. A general procedure consists...
In this paper, we introduce the notion of lattice-valued fuzzy residual finite automaton (LRFA) and the LRFA-regular language with membership values in a complete residu-ated lattice. Next, we define saturation operator and reduction operator on lattice-valued finite automata(Li'M), which provide a way to simplify LRFA based on their closure properties in LRFA. At last, we define the canonical LRFA...
This paper proposes a new active fault-tolerant control (FTC) using fuzzy predictive logic. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based approach using fuzzy modeling and fault isolation uses a fuzzy decision making approach. The information obtained on the FDI step is used to select the model...
In this paper we develop convex delay-dependent conditions in terms of linear matrix inequalities (LMIs) for the synthesis of fuzzy stabilizing feedback controllers. The condition is developed from a novel Lyapunov-Krasovskii fuzzy function. We consider that the T-S fuzzy model represents the nonlinear system only inside a region of validity. Because of this, we determine a domain of stability inside...
A near optimal control technique for robotic manipulator with completely unknown dynamics is described in this work. Obtaining the optimal control law u∗ depends on solving Hamilton Jacobi Bellman equation but getting an analytic solution is not possible for unknown models. It is shown that instead of solving HJB equation analytically, the optimal control law can be obtained through learning of a...
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