New Mathematics and Natural Computation

We use techniques of group decision making and consensus under fuzzy preferences and fuzzy majority developed so as to compare five methods used for determining the degree of causality of eight variables for democratic consolidation. In particular for Q denoting the linguistic variable 'most', we determine the fuzzy Q-core, i.e. the fuzzy subset of the set of causal variables that are not defeated (are undominated or unpreferred) by the methods. We also calculate the Q-consensus winner, i.e. the fuzzy subset of causal variables yielding the extent to which one variable is preferred to another, where 'preferred' here means stronger cause for democratic consolidation. Finally, we use the techniques of fuzzy outranking relations in preference modeling to generate relational systems of fuzzy preferences by the five methods used to determine the degree of causality of the causal variables.

In this paper, we prove a fuzzy version of Arrow's Theorem that contains the crisp version. We show that under our definitions, Arrow's Theorem remains intact even if levels of intensities of the players and levels of membership in the set of alternatives are considered.

The present paper studies several compactness and continuity notions based on the quadruple M = (L,≤,⊗,*), where (L,≤), ⊗ and * respectively denote a complete lattice and binary operations on L, satisfying some further axioms, was introduced by Höhle and Sostak.^1,2

In this paper, a new compactness in L-fuzzy topological spaces called strong Lowen's compactness is introduced. It is a kind of compactness lying between Lowen's compactness and SR-compactness in L-fuzzy topological spaces. Its topological properties are systematically studied. And some good results are obtained.

In decision-making processes, it often occurs that the decision maker is asked to pairwise compare alternatives. His/her judgements over a set of pairs of alternatives can be collected into a matrix and some relevant properties, for instance, consistency, can be estimated. Consistency is a desirable property which implies that all the pairwise comparisons respect a principle of transitivity. So far, many indices have been proposed to estimate consistency. Nevertheless, in this paper we argue that most of these indices do not fairly evaluate this property. Then, we introduce a new consistency evaluation method and we propose to use it in group decision making problems in order to fairly weigh the decision maker's preferences according to their consistency. In our analysis, we consider two families of pairwise comparison matrices: additively reciprocal pairwise comparison matrices and multiplicatively reciprocal pairwise comparison matrices.

A large number of South Africa's elephants can be found on small wildlife reserves. The large nutritional demands and destructive foraging behavior of elephants can threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of a reserve as well as which area they will move to next could be useful. The goal of this study was to train a recurrent neural network to predict an elephant herd's next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing future impact of elephant populations on different areas of the reserve. Particle swarm optimization (PSO), PSO initialized backpropagation (PSO-BP) and PSO initialized backpropagation through time (PSO-BPTT) algorithms are used to adapt the recurrent neural network's weights. The effectiveness of PSO, PSO-BP and PSO-BPTT for training a recurrent neural network for elephant migration prediction is compared and PSO-BPTT produces the most accurate predictions at the expense of more computational cost.

The world is increasingly dependent on critical infrastructures such as the electric power grid, water, gas and oil transport systems. Due to this increasing dependence and inadequate infrastructure expansion, these systems are becoming increasingly stressed. These additional stresses leave these systems less resilient to external faults, both accidental and malicious than ever before. As a result of this increased vulnerability, many critical infrastructures are becoming susceptible to cascading failures, where an initial fault caused by an external force may induce a domino-effect of further component failures. An important implication is that traditional infrastructure risk analysis methods, often relying on Monte Carlo sampling of fault scenarios, are no longer sufficient. Instead, systematic analysis based on worst-case attacks by intelligent adversaries is essential. This paper describes a coevolutionary methodology to simultaneously discover low-effort high-impact faults and corresponding means of hardening infrastructures against them. We empirically validate our methodology through an electric power transmission system case study.

In this paper, we propose a risk-based framework for military capability planning. Within this framework, metaheuristic techniques such as Evolutionary Algorithms are used to deal with multi-objectivity of a class of NP-hard resource investment problems, called The Mission Capability Planning Problem, under the presence of risk factors. This problem inherently has at least two conflicting objectives: minimizing the cost of investment in the resources as well as the makespan of the plans. The framework allows the addition of a risk-based objective to the problem in order to support risk assessment during the planning process. In other words, with this framework, a mechanism of progressive risk assessment is introduced to capability planning.

We analyze the performance of the proposed framework under both scenarios: with and without risk. In the case of no risk, the purpose is to study several optimization-related aspects of the framework such as convergence, trade-off analysis, and its sensitivity to the algorithm parameters; while the second case is to demonstrate the ability of the framework in supporting risk assessment and also robustness analysis.

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

A modular learning design for classifying aircraft flight data in time-series prediction is proposed in this paper. This is part of the decision support system to assist landing signal officers in guiding aircraft to land on aircraft carriers. NeuroFuzzy systems are used to emulate the flight patterns for future real-time flight prediction. To improve the learning efficiency, a two stage modular learning design is proposed. The data to be learned is first decomposed into categories in accordance to their physical structure. Each module of data is presented to a different NeuroFuzzy system for learning purpose. Individually trained modules are modeled as genetic chromosomes. Genetic algorithm is used to produce a chromosome module that represents a generalization of all the trained modules. As compared with the non-modular approach, the modular approach offers comparable prediction performance with significantly lower overall computation time. We show that the reduction in computation time with the modular approach is exponential as the problem size increases. Navy aircraft data were used to validate the effectiveness of the modular design and the result is consistent and promising.

Face expression recognition is an active area of research with several fields of applications, ranging from emotion recognition for advanced human computer interaction to avatar animation for the movie industry. This paper presents a review of the state-of-the-art emotion recognition based on the visual analysis of facial expressions. We cover the main technical approaches and discuss the issues related to the gathering of data for the validation of the proposed systems.