Brain Signals Classification Based on Fuzzy Lattice Reasoning

Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques.

Operations on complex intuitionistic fuzzy soft lattice ordered group and CIFS-COPRAS method for equipment selection process

This paper introduces some new operations on complex intuitionistic fuzzy lattice ordered groups such as sum, product, bounded product, bounded difference and disjoint sum, and verifying its pertinent properties. The research exhibits the CIFS-COPRAS algorithm in a complex intuitionistic fuzzy soft set environment. This method was furthermore applied for the equipment selection process.

Fuzzy Lattice Order Group Decision for Preference Ranking in Conflict Analysis

Methods of fuzzy multi-objective lattice order decision making (F-MOLODM) for analyses of the fuzzy and intransitive preferences of decision makers involved in a conflict analysis are devised for the preference ranking of the states in a conflict, which defines the trapezoidal fuzzy number of the preference and constructs a multi-objective group decision-making fuzzy preference matrix. An algorithm for F-LOGDM is proposed to capture uncertainty and intransitive decision problems, and the relationship among lattice elements of the preference structure under fuzzy environments is defined. The application of these decision technologies to the square dance conflict illustrate how the method proposed in this paper can be utilized in practice. The performance of the lattice order preference ranking method applied to the conflict is compared with that of traditional methods. Research shows that either lattice order preference ranking, or traditional methods acquires Nash stability. However, the stable state under traditional methods will be reached only if all players pay a certain price.