2,979 publications from this institution
Fuzzy cognitive maps (FCMs) are directed graphs with multiple nodes, making them well-suited for addressing multivariate time series (MTS) forecasting problems. When forecasting MTS, it is crucial to treat each vector of the MTS as a whole, considering both the causalities between different variables of the vector at a timepoint (spatial relationship) and the causalities between multiple historical vectors and future vector (temporal relationship). Existing FCM-based MTS forecasting models often fail to treat the vectors as a whole and do not distinctly reflect the temporal relationship and spatial relationship in MTS. To address these limitations, this paper introduces the concept of composite fuzzy cognitive maps (CFCMs). A CFCM comprises two layers of FCMs: the layer-1 FCM describes the temporal relationship in an MTS, while the layer-2 FCM describes the spatial relationship. By embedding the layer-2 FCMs into the nodes of the layer-1 FCM, the relationships within the MTS can be separately reflected while still treating each vector as a whole. In this structure, the nodes of the layer-1 FCM represent historical vectors used to forecast the future vector, and each node of the layer-1 FCM corresponds to a layer-2 FCM whose nodes represent the variables of the vector at a specific historical timepoint in the MTS. Based on the novel CFCM concept, this paper proposes a new MTS forecasting model that can distinctly reflect the temporal and spatial relationships in an MTS and utilize multiple historical vectors to forecast the future vector. Experimental results demonstrate the effectiveness of the proposed MTS forecasting model.
In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant.
<title>Abstract</title> The federated averaging algorithm (FedAvg) is extensively used for multi-sensor data modeling but often overlooks the unique characteristics of local models when privacy and data security are not considered. This study introduces a novel federated learning algorithm built upon the FedAvg framework, which emphasizes the specificity of each local model to optimize global knowledge aggregation. The algorithm's effectiveness is demonstrated through an air quality index prediction problem, showcasing superior prediction performance and robustness in noisy data scenarios. Additionally, the study delves into the reliability and robustness of the proposed approach, addressing the prevalent notion that centralized learning methods often surpass federated learning when data security is not a concern. Our experiments affirm the necessity and superiority of federated learning methods, even in the absence of privacy considerations, by effectively managing real-world noisy data.
In this study, we offer a general view at the area of fuzzy modeling and fuzzymodels, identify the visible development phases and elaborate on a new and promisingdirections of system modeling by introducing a concept of granular models. Granularmodels, especially granular fuzzy models constitute an important generalization of existingfuzzy models and, in contrast to the existing models, generate results in the form ofinformation granules (such as intervals, fuzzy sets, rough sets and others). We present arationale and deliver some key motivating arguments behind the emergence of granularmodels and discuss their underlying design process. Central to the development of granularmodels are granular spaces, namely a granular space of parameters of the models and agranular input space. The development of the granular model is completed through anoptimal allocation of information granularity, which optimizes criteria of coverage andspecificity of granular information. The emergence of granular models of type-2 and type-n,in general, is discussed along with an elaboration on their formation. It is shown thatachieving a sound coverage-specificity tradeoff (compromise) is of paramount relevance inthe realization of the granular models.
Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.
