The problem of prescribed performance control (PPC) for the MIMO block-triangular nonlinear systems under output constraints is investigated in this article. It is focused on the scenario where the references are not known in advance. This renders the related solutions infeasible and becomes more challenging under the totally unknown and inherently nonlinear dynamics of the system. To overcome this challenge, a novel robust decoupling PPC strategy is developed in this article, in which an online boundary generation scheme and a smoothly constraint switching rule are devised and introduced. The resulting controller ensures that the system outputs evolve within their respective constraint bands and track the references with the predetermined overshoot, settling time and accuracy. Moreover, it is independent of function approximation, parameter identification, or disturbance estimation, despite the unbounded nonlinearities, unmatched disturbances and unknown dynamics. A comparative experiment on a 2DSFL robot is conducted to show the efficacy and superiority of our low-complexity high-performance control approach.
In order to deal with a complex decision making problem, a group of experts are commonly invited to express their opinions and reach a final decision. For the purpose of building consensus among the members of the group, it is requisite to include iterative mechanisms of brain storming. The particle swarm optimization (PSO) method can be used to model the interactive process of forming decisions. In this paper, we propose a modified consensus model of group decision making augmented by an allocation of information granularity. Under a level of information granularity, it is found that the consistency indexes of randomly created multiplicative reciprocal matrices in the analytic hierarchy process may be bigger than unity. To alleviate this limitation, a modified objective function is proposed, and it is optimized by using the modified PSO method. The information granularity is allocated by considering the reciprocity of preference relations. Some comparative studies are carried out to illustrate the proposed consensus model through numerical examples. The observations reveal that a more consistent decision can be achieved by the proposed approach.
Purpose This paper sets out to design hyperbox classifiers of high interpretation capabilities. They are based on a collection of hyperboxes – generic and highly interpretable geometric descriptors of data belonging to a certain class. Such hyperboxes directly translate into conditional statements (rules) taking on the well‐known format “if feature 1 assumes values in [ a , b ] and feature 2 assumes values in [ d , f ] and … and feature n assumes values in [ w , z ] then class ω ” where the intervals ([ a , b ],…[ w , z ]) are the respective edges (features) of the corresponding hyperbox. Design/methodology/approach The proposed design process of hyperboxes consists of two main phases. In the first phase, a collection of “seeds” of the hyperboxes is constructed through data clustering being realized by means of the fuzzy C‐means algorithm. During the second phase, the hyperboxes are “grown” (expanded) by applying mechanisms of genetic optimization (and genetic algorithm, in particular). Findings It is demonstrated how the underlying geometry of the hyperboxes supports an immediate interpretation of arrhythmia data by linking the ranges of the features (parameters of the ECG signal) forming the edges of the hyperboxes with the two classes of the signals (normal – abnormal). A collection of comprehensive experiments offers an interesting insight into the geometry of the individual categories of the ECG signals and discusses how the resulting hyperbox classifiers link their geometric properties with the obtained classification rates. Research limitations/implications The structure of the classifier is essential to enhance interpretation capabilities of the architecture and generate a collection of “if‐then” classification rules. Originality/value The study addresses an issue of design of highly interpretable, granular classifiers with the use of the technology of computational intelligence and evolutionary optimization, in particular.
The authors study models of referential structures and referential modes of reasoning for fuzzy data. The style of information processing considered is aimed at reasoning about some global properties of the spaces in which the fuzzy data are situated. The distributed models, designed in terms of logic-based neural networks, realize the mapping of these properties between the spaces. The scheme embraces reasoning about similarity, difference, dominance, and inclusion of the conclusions that is based on the corresponding relationships between the universes and their strength discerned for the antecedents. For instance, the conclusions issued within the scheme are of the form: b and b' are similar, b and b' are different, etc. It is shown that by considering the available degrees of satisfaction of these properties, the corresponding fuzzy sets of conclusion can be determined.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
In spite of their striking diversity, numerous tasks and architectures of intelligent systems such as those permeating multivariable data analysis (e.g., time series, spatio-temporal, and spatial dependencies), decision-making processes along with their models, recommender systems and others exhibit two evident commonalities. They promote human centricity and vigorously engage perceptions (rather than plain numeric entities) in the realization of the systems and their usage. Information granules play a pivotal role in such settings. In the sequel, Granular Computing delivers a cohesive framework supporting a formation of information granules and facilitating their processing. We exploit two essential concepts of Granular Computing. The first one, formed with the aid of a principle of justifiable granularity, deals with the construction of information granules. The second one, based on an idea of an optimal allocation of information granularity, helps endow constructs of intelligent systems with a very much required conceptual and modeling flexibility. The talk covers in detail two representative studies. The first one is concerned with a granular interpretation of temporal data where the role of information granularity is profoundly visible when effectively supporting human centric description of relationships existing in data. In the second study being focused on the Analytic Hierarchy Process (AHP) used in decision-making, we show how an optimal allocation of granularity helps facilitate collaborative activities (e.g., consensus building) in group decision-making.
