Publications

In this section an overview of learning methods, applied to the automated acquisition of knowledge in fuzzy inference systems, is given. The peculiar features added to the learning problem by the fuzziness of the target knowledge will be stressed. Learning approaches have had their origins either inside the fuzzy sets community, where the primary aim was to ease the burden of manually developing a fuzzy rule base, or in the machine learning field, where the main goal was to extend existing learning algorithms to cope with uncertainty in the data.

A timely and effective scheduling of the byproduct gas system plays a pivotal role in realizing intelligent manufacturing and energy conservation in the steel industry. In order to realize real-time dynamic scheduling of the blast furnace gas (BFG) system, a granular prediction and dynamic scheduling process based on adaptive dynamic programming is proposed in this paper. To reflect the specificity of production reflected in the fluctuation of data, a series of information granules is constructed and described. In the dynamic scheduling phase, based on the granular feature description, a scheduling action network is established and further updates of information granules are realized. Considering a slow adjustment process and delay characteristics of the BFG system, the cumulative reward of the critic network is calculated on the basis of the data partition to construct a tendency attenuation-based cost function. In order to determine the future trends of the gas tank level that targets real-time determination of the scheduling moment, a reinforcement learning-based granulation and prediction process is also proposed. To demonstrate the performance of the proposed method, a number of comparative experiments are presented by using the practical industrial data. The results indicate that the proposed method exhibits high accuracy and can deliver an effective solution to justified scheduling of the BFG system.

This paper introduces a novel incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting (MCS) problems, enabling decision makers to progressively provide assignment example preference information. Specifically, we first construct a max-margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process. Using the optimal objective function value of the max-margin optimization-based model, we devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration within the framework of uncertainty sampling in active learning. Once the termination criterion is satisfied, the sorting result for non-reference alternatives can be determined through the use of two optimization models, i.e., the max-margin optimization-based model and the complexity controlling optimization model. Subsequently, two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences, considering different termination criteria. Ultimately, we apply the proposed approach to a credit rating problem to elucidate the detailed implementation steps, and perform computational experiments on both artificial and real-world data sets to compare the proposed question selection strategies with several benchmark strategies.

Traditional detection and prewarning methods based on a single atmospheric electric field (AEF) data source often fail to accurately assess thunderstorm weather conditions. This article proposes a thunderstorm detection method that employs weighted multisource fusion of precipitation and AEF features, achieving a high-precision evaluation through multimodal feature fusion and machine learning model optimization. Based on the detection range of AEF apparatus, spatially matched radar chart precipitation data are extracted to construct physically meaningful precipitation statistic features and time-varying AEF features. Using feature importance analysis, the physical contribution of each feature is determined, and these features are weighted and fused accordingly to generate enhanced features. Furthermore, we establish an improved gradient boosting decision tree model that enhances classification performance in the weighted feature space by adaptively adjusting the learning rate. Experimental results demonstrate that this method achieves a highly competitive F1-score of 0.92 in thunderstorm recognition tasks, representing a 35.3% improvement over the traditional AEF threshold method. The proposed weighted fusion framework provides a novel solution for thunderstorm detection and prewarning through the synergistic use of ground-based AEF and radar-based precipitation data.

We propose a new category of neurofuzzy networks—self-organizing neural networks (SONN) with fuzzy polynomial neurons (FPNs) and discuss a comprehensive design methodology supporting their development. Two kinds of SONN architectures, namely a basic SONN and a modified SONN architecture are discussed. Each of them comes with two topologies such as a generic and advanced type. Especially in the advanced type, the number of nodes in each layer of the SONN architecture can be modified with new nodes added, if necessary. SONN dwells on the ideas of fuzzy rule-based computing and neural networks. The architecture of the SONN is not fixed in advance as it usually takes place in the case of conventional neural networks, but becomes organized dynamically through a growth process. Simulation involves a series of synthetic as well as real-world data used across various neurofuzzy systems. A comparative analysis shows that the proposed SONN are models exhibiting higher accuracy than some other fuzzy models.

Quantifying the uncertainty of information distributions containing randomness, imprecision, and fuzziness is the premise of processing them. A useful information representation in the field of intelligent computing are information granules, which optimize data from the perspective of specificity and coverage. We introduce information granularity into evidential information and model the basic probability assignment (BPA) as a weighted information granules model. Based on the proposed model, a new uncertainty measure of BPA is derived from the quality evaluation of granules. In addition, the proposed measure is extended to fuzzy evidential information distributions. When the Fuzzy BPA (FBPA) degenerates into the Probability Mass Function (ProbMF) and Possibility Mass Function (PossMF), the proposed method degenerates to Gini entropy and Yager's specificity measure, respectively. We use a refined belief structure to interpret the meaning of FBPA in the transfer belief model, and verify the validity of the proposed method by analyzing its properties and presenting numerical examples. The concept of information granule is used for the first time to model focal set and beliefs. Compared with Shannon entropy based information measures, the proposed method provides a novel perspective on the relationship between randomness, imprecision, and fuzziness in FBPA.