Publications

One drawback of using the existing one-step forecasting models for long-term time series prediction is the cumulative errors caused by iterations. In order to overcome this shortcoming, this article proposes a trend-fuzzy-granulation-based adaptive fuzzy cognitive map (FCM) for long-term time series forecasting. Different from the original FCM-based forecasting models, a class of trend fuzzy information granules is built to represent the trend, fluctuation range, and trend persistence of various segments of time series, which are more instrumental and comprehensive than simple magnitude information. Thus, the proposed forecasting model is a granular model according to the form of its inputs and outputs. In an original FCM-based forecasting model, the causal relationships among concepts remain unchanged throughout the training of the whole dataset, however, in reality, the causal relationships may change with the state of concepts. Therefore, it is unreasonable to use the invariable causal relationships which often result in poor predictions. In view of this, we construct an adaptive FCM where different causal relationships are built to forecast concepts of different states. This is the first time to forecast trend fuzzy information granules using an adaptive FCM. Compared with the existing classical forecasting models, the proposed forecasting model achieves superior performance which is verified through a series of experimental studies.

Efficient scheduling is of great significance to rationally make use of scarce satellite resources. Task clustering has been demonstrated to realize an effective strategy to improve the efficiency of satellite scheduling. However, the previous task clustering strategy is static. That is, it is integrated into the scheduling in a two-phase manner rather than in a dynamic fashion, without expressing its full potential in improving the satellite scheduling performance. In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs). First, we develop a formal model for the scheduling of Earth observing satellites. Second, we analyze the related constraints involved in the observation task clustering process. Thirdly, we detail an implementation of the dynamic task clustering strategy and the adaptive Simulated Annealing algorithm. The adaptive Simulated Annealing algorithm is efficient, with the endowment of some sophisticated mechanisms, i.e. adaptive temperature control, tabu-list based revisiting avoidance mechanism, and intelligent combination of neighborhood structures. Finally, we report on experimental simulation studies to demonstrate the competitive performance of ASA-DTC. Moreover, we show that ASA-DTC is especially effective when SOSPs contain a large number of targets or these targets are densely distributed in a certain area.

Anchor-based strategies have been widely used to accelerate spectral clustering, yet their effectiveness is directly affected by the quality of the selected anchors. Random sampling has become one of the most important anchor determination methods due to its efficiency. However, the anchors obtained by a single random sampling often fail to adequately capture the topological structure of the original data, making it difficult for the constructed anchor graph to achieve satisfactory clustering performance. To solve this problem, we propose a novel spectral embedding representation model based on random anchor graph aggregation (RAGA), in which an aggregated anchor graph can be produced to obtain enhanced sample representation capability. Specifically, we perform multiple random samplings to make the distribution of the selected anchors approximate the original data within a reasonable sampling time. Subsequently, adaptive weighted learning is performed on the contribution of the constructed multiple anchor graphs, and then an aggregated anchor graph can be formed, which can portray the topological structure of the original samples more precisely. In addition, spectral embedding and spectral rotation are integrated into a joint learning framework to reduce the model learning error accumulation caused by the traditional two-stage framework. Notably, we propose a rigorous theorem for analyzing the approximation of samples by the selected anchors in multiple random samplings. Our proposed RAGA maintains the speed advantage of random sampling while obtaining a high-quality aggregated anchor graph, enabling it to handle large-scale data scenarios. Experimental results on several benchmark datasets show that the RAGA model outperforms other state-of-the-art (SOTA) anchor graph-based clustering methods.

Granulation of information by using type-2 fuzzy sets is receiving more attention nowadays. This is due to the superior capability of type-2 fuzzy sets in handling the data uncertainty. From a theoretical perspective, a set containing type-2 fuzzy sets has no trivial geometric structure; therefore, a proper metric cannot be easily defined. As a consequence, common pattern recognition systems, which in one way or another rely on some (geo)metric structure of the input space, are not easily applicable to a space of type-2 fuzzy sets. In this paper, we study the problem of designing a classifier in the input space of type-2 fuzzy sets. Type-2 fuzzy sets are hence interpreted as (granular) patterns forming a given input dataset. By decomposing a type-2 fuzzy set into a sequence of simpler (lower type) fuzzy sets, we explore the possibility of defining and building dissimilarity and kernel-based classification systems on input spaces of type-2 fuzzy sets. Such an interpretation provided in terms of sequences allows us to conceive an effective sequence matching strategy, which can be suitably embedded into well-established pattern recognition systems. We support the methodological developments by performing experiments on synthetically generated classification problems for datasets composed of type-2 fuzzy sets, with adjustable and controlled level of difficulty. Results are promising and suggest to further investigate on the possibility of interpreting type-2 fuzzy sets as input patterns of a given data-driven inference system.