This paper studies the consensus problem of multi-agent systems with nonuniform time-delays and dynamically changing topologies. A linear consensus protocol is introduced to realize local control ...strategies for these second-order discrete-time agents. By model transformations and applying the properties of nonnegative matrices, sufficient conditions are derived for state consensus of the systems. It is shown that arbitrary bounded time-delays can safely be tolerated, even though the communication structures between agents dynamically change over time and the corresponding directed graphs may not have spanning trees. Finally, a numerical example is included to illustrate the obtained results.
•We present two new hybrids of FCM and improved self-adaptive PSO.•The methods are based on the FCM–PSO algorithm.•We use FCM to initialize one particle to achieve better results in less ...iterations.•The new methods are compared to FCM–PSO using many real and synthetic datasets.•The proposed methods consistently outperform FCM–PSO in three evaluation metrics.
Fuzzy clustering has become an important research field with many applications to real world problems. Among fuzzy clustering methods, fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency, although it shows some weaknesses, particularly its tendency to fall into local minima. To tackle this shortcoming, many optimization-based fuzzy clustering methods have been proposed in the literature. Some of these methods are based solely on a metaheuristic optimization, such as particle swarm optimization (PSO) whereas others are hybrid methods that combine a metaheuristic with a traditional partitional clustering method such as FCM. It is demonstrated in the literature that methods that hybridize PSO and FCM for clustering have an improved accuracy over traditional partitional clustering approaches. On the other hand, PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques. Another problem with PSO-based clustering is that the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. In this paper we introduce two hybrid methods for fuzzy clustering that aim to deal with these shortcomings. The methods, referred to as FCM–IDPSO and FCM2–IDPSO, combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions. Experiments using two synthetic data sets and eight real-world data sets are reported and discussed. The experiments considered the proposed methods as well as some recent PSO-based fuzzy clustering methods. The results show that the methods introduced in this paper provide comparable or in many cases better solutions than the other methods considered in the comparison and were much faster than the other state of the art PSO-based methods.
We consider positive rules in which the conclusion may contain existentially quantified variables, which makes reasoning tasks (such as conjunctive query answering or entailment) undecidable. These ...rules, called ∀∃-rules, have the same logical form as tuple-generating dependencies in databases and as conceptual graph rules. The aim of this paper is to provide a clearer picture of the frontier between decidability and non-decidability of reasoning with these rules. Previous known decidable classes were based on forward chaining. On the one hand we extend these classes, on the other hand we introduce decidable classes based on backward chaining. A side result is the definition of a backward mechanism that takes the complex structure of ∀∃-rule conclusions into account. We classify all known decidable classes by inclusion. Then, we study the question of whether the union of two decidable classes remains decidable and show that the answer is negative, except for one class and a still open case. This highlights the interest of studying interactions between rules. We give a constructive definition of dependencies between rules and widen the landscape of decidable classes with conditions on rule dependencies and a mixed forward/backward chaining mechanism. Finally, we integrate rules with equality and negative constraints to our framework.
This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential ...value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.
•We concern with a fleet operator considering to adopt an alternative-fuel vehicle.•The problem is a multi-expert and multicriteria problem with conflicting criteria.•1st major contribution is ...identification and classification of evaluation criteria.•2nd major contribution is a hierarchical hesitant fuzzy linguistic model.•The paper presents a real world application with sensitivity and scenario analyzes.
Decision on alternative-fuel vehicles is one of the most important problems for fleet operations. In this paper we propose a hierarchical hesitant fuzzy linguistic model that captures hesitant linguistic evaluations of multiple experts on multiple criteria for alternative-fuel vehicles. We apply the proposed model on the alternative-fuel vehicle selection problem of a home health care service provider in the USA. The results show that an electric vehicle is the best fit for the application in today’s conditions. We also show robustness of the decision through a sensitivity analysis as well as analyze three scenarios representing possible changes in conditions.
•Survey of the main existing techniques for concept lattices reduction.•Classification of techniques in three classes based on seven dimensions.•Analyzing reduction techniques with formal concept ...analysis.•Considerations are carried out about computational complexity and feasibility.
Formal concept analysis (FCA) is currently considered an important formalism for knowledge representation, extraction and analysis with applications in different areas. A problem identified in several applications is the computational cost due to the large number of formal concepts generated. Even when that number is not very large, the essential aspects, those effectively needed, can be immersed in a maze of irrelevant details. In fact, the problem of obtaining a concept lattice of appropriate complexity and size is one of the most important problems of FCA. In literature, several different approaches to control the complexity and size of a concept lattice have been described, but so far they have not been properly analyzed, compared and classified. We propose the classification of techniques for concept lattice reduction in three groups: redundant information removal, simplification, and selection. The main techniques to reduce concept lattice are analyzed and classified based on seven dimensions, each one composed of a set of characteristics. Considerations are made about the applicability and computational complexity of approaches of different classes.
In this paper, we study the cooperative global robust output regulation problem for a class of lower triangular nonlinear uncertain multi-agent systems. We first introduce a type of distributed ...internal model to convert the cooperative global robust output regulation problem into a global robust stabilization problem of a so-called augmented multi-agent system which is in the block lower triangular form. We then further show that, under a set of standard assumptions, the augmented multi-agent system can be globally stabilized via a distributed state feedback control law by developing a block backstepping technique. A special case of the main result of this paper leads to the solution of the leader-following global robust consensus problem for a class of nonlinear uncertain multi-agent systems.
•Model interpretability.•Safety assessment model for complex system with modeling transparency and traceability.•Safety assessment model that users can understand and accept.•Optimization model based ...on the gradient of output.•The convergence of the optimization method.
Safety assessment is an important aspect of health management for complex system. Belief rule base (BRB) is one of the expert systems which can handle uncertainty, ambiguity and conflicting information. In safety assessment based on BRB, its initial parameters are determined by experts and then modified by optimization models. In current studies, some intelligent optimization algorithms are applied, and the parameters are trained based on the generated random population. The optimized parameters and structure of BRB by these optimization models may lose physical meaning, and it loses interpretability. Thus, to ensure the modeling transparency and traceability, a safety assessment model based BRB with a new optimization method based on the method of feasible direction (MFD) is developed for the first time, where the gradient of output to model parameters is deduced. Moreover, the convergence of the optimization method is proved to ensure that the optimized parameters are optimal solutions. In the new optimization model, the parameters are trained based on the output gradient of BRB analytical model that can keep the transparency of modeling process and ensure the interpretability of the constructed safety assessment model. A case study is conducted to illustrate the effect of the developed safety assessment model.
In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use ...are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.
This research discusses the development of an expert system to diagnose peanut disease using Case-Based Reasoning (CBR) and Nearest Neighbor Similarity. CBR is a computer reasoning system using old ...knowledge to overcome new problems. It provides solutions by looking at the closest old case to new case. The diagnosis process is carried out by entering a new case containing the symptoms to be diagnosed into the system, then calculating similarity values between new cases on a case base using the nearest neighbor method. The average test results of the system to make an initial diagnosis of peanut disease indicate that the system is able to correctly recognize 100% peanut disease. Accuracy calculation uses the nearest neighbor similarity method with a threshold of 0.5, 0.6 and 0.7 respectively 97.22, 88.89%, and 80.55%.
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