This paper studies the partial synchronization problem of multi-layer mixed valued logical networks (MMVLNs) under state-circulated control (SCC). In the proposed method, first, MMVLNs are transformed into multi-layer k-valued logical networks (MKVLNs) using two methods, dimension expansion and state constraint, and the state transition matrix is constructed using the algebraic state space representation (ASSR) method. Then, various synchronization scenarios in the multi-layer network are integrated, and the necessary and sufficient conditions for the partial synchronization of the MKVLNs under SCC are proposed. Subsequently, based on the SCC, the necessary and sufficient conditions for partial synchronization of the MKVLNs under different complex conditions (disturbances and asynchronous update) are investigated. Meanwhile, multiple graph-based algorithms for the largest control invariant set (LCIS) are developed during the partial synchronization research. Finally, several numerical examples verify the results of partial synchronization.

With the rapid development of the Internet of Things, a large amount of real-time traffic data has been collected. How to fully utilize these traffic data for rational planning of intelligent transportation systems is an important issue. Graph learning has proved to be potential tools for modeling complex spatiotemporal relationships in addressing traffic flow prediction problems. Existing methods usually relied on the graph structure constructed from predefined distance information between different regions. However, affected by factors such as road capacity, traffic rules, etc., distance bias is nontrivial to affect traffic flow in reality. Besides, the distance-based static transportation network structure is also difficult to describe the dynamic and uncertainty of road traffic in complex situations. To address the aforementioned issues, this article proposes an enhanced graph generation approach for traffic flow prediction, which is based on Temporal-Frequency Self-Supervised Guided Graph Learning (TFSG). The method progressively guides the feature latent representation to approach the target latent representation in both the time domain and frequency domain. Meanwhile, a multihead self-attention mechanism is introduced to capture dependencies among different time series patches, constructing an enhanced graph structure for downstream prediction tasks. Experimental results first independently evaluated the qualities of the generated graphs. Subsequently, by using five public traffic datasets, the feasibility and effectiveness of the proposed method are further verified.

Multi-Access Edge Computing (MEC) is a key solution to address the computational resource constraints of User Equipment (UE). To achieve generalization on unseen scenarios, existing gradient-based meta-reinforcement learning (Meta-RL) methods rely on fine-tuning model parameters via gradient descent during the testing phase. However, in real-world MEC environments, parameter fine-tuning is frequently impractical owing to device heterogeneity, computational resource constraints, and stringent real-time requirements. Moreover, even when fine-tuning is possible, the additional processing overhead can significantly undermine the latency advantages provided by edge computing. To overcome these limitations, we focus on the zero-shot generalization problem in MEC task offloading, which aims to ensure the model's generalization ability without performing parameter fine-tuning. To this end, we propose a Causal Reinforcement Learning (CRL) method that learns a distribution-invariant offloading policy to achieve zero-shot generalization. Our method dynamically identifies task distribution shifts as confounding factors via a self-attention-based Soft Mask. Then, we apply causal interventions using these confounders to disentangle their effect on the policy, allowing the model to learn an invariant policy across different task distributions. Simulation results demonstrate that the proposed method achieves strong generalization performance in zero-shot settings across multiple heterogeneous MEC task offloading scenarios.

Traffic flow Forecasting is essential in intelligent transportation systems. Although graph neural networks perform well with non-Euclidean traffic data, they exhibit limits in accurately capturing complex spatio-temporal dependencies. Most current methodologies assume instantaneous propagation of traffic flow information, neglecting the delay in information propagation between nodes. Moreover, traditional graph convolutional models capture spatial dependencies using static adjacency matrices, overlooking the dynamic correlations among nodes over time. To address these challenges, this paper proposes a delay propagation spatio-temporal graph convolutional network (DPSTGC) for traffic prediction. By incorporating the delay propagation of traffic flow information into a directed graph of the road network, this model accurately captures the spatio-temporal dependencies of traffic flows. To refine the relationships between nodes, adaptive graph convolution network is employed to learn the dynamic correlation between nodes. The alternating application of temporal gated convolutions and spatial structure significantly enhances the model’s capacity to interpret spatio-temporal information. Furthermore, the effectiveness of the Delay-aware Directed Graph Attention (DDGA) is further interpreted from a causality perspective. Finally, the proposed model is evaluated on four real-world traffic datasets for experimental validation. Experimental results demonstrate that DPSTGC proficiently captures spatio-temporal information and achieves excellent performance.

Anomaly detection is an important data-mining task closely related to specific applications. In recent years, spatio-temporal data deriving from various networks has been collected from many real-world scenarios, but how to detect anomalies in such data is still an open problem. In this article, we propose a novel anomaly detection framework, Multi-scale Fuzzy Contrastive Anomaly Detection (MFCAD), by capturing anomalous patterns of data from multiple spatio-temporal scales so as to learn distinguishable feature representations. Different from conventional reconstruction- or prediction-based anomaly detection methods, this approach is not concerned with the consistency of the encoded representation of the implicit layer and the discriminability of the implicit layer of the anomalies. MFCAD implements a more discriminative representation using fuzzy contrastive learning and explicitly performs anomaly detection in the potential space by measuring the distance between outliers and implicit values. This proposed method has been applied for the anomaly detection of 5G mobile network clusters (5G-MNCs) in China Mobile. Furthermore, in order to validate the generalizability of the proposed method, it is further tested on public datasets and experiment results show the promising performance.

High fluctuation and self-similarity are typical characteristics of financial time series. Furthermore, affected by market environment, such as regular announcement of important economic data, time/date-sensitive fluctuations commonly exist in financial time series. However, the existing learning models were usually lack the consideration of essential characteristics of financial data, where both the fusion learning of multiple temporal features and the necessary attention to time-sensitive fluctuations were ignored. Inspired by this, to represent the temporal characteristics of self-similarity and reveal intrinsic feature details, in this article, time series and its features are converted into visibility graphs using the technique of Gramian Angular Fields, based on which convolutional long short-term memory (ConvLSTM) is applied to implement multifeature fusion learning. Moreover, to capture the time/date-sensitive fluctuation existing in financial time series, a subspace decomposition composed of the fuzzy control mechanism is first introduced into the ConvLSTM model, which considerably improves the prediction performance. On the basis of the proposed learning model, a concise intelligent trading strategy is designed. By using real foreign exchange data, various experiments are implemented to show the effectiveness of the proposed model.

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.

Fuzzy cognitive maps (FCMs) as a kind of knowledge-based tools are widely applied to model complex dynamical systems using causal relations. Besides the representation and reasoning of systems behaviors, how to control the given systems into a desirable target by causal objects established by FCMs is also an open problem. Although, so far, there are some existing works about the applications of FCMs on the control-related problems, it is still a lack of the theoretical analysis in this domain. In this paper, the controllability of k-valued FCMs is studied. To improve the universality of models, a temporal extension of generalized FCMs is implemented. By means of semitensor product, the algebraic representation of k-valued FCMs with controls is established and a generalized formula of control-depending network transition matrices is achieved. A necessary and sufficient condition is proved to determine the control-depending fixed points of k-valued FCMs with temporalization. By utilizing three kinds of controls, the controllability of the discrete FCMs is discussed, respectively. The reachability condition of a specific target state from a given initial state at time s is studied, and the reachable set along with the corresponding reachable probability are also provided by analytic formula. Results provide a way to make FCMs evolving into the designed states by controls, which can further conduct the behaviors of the modeled systems in reality. Examples are shown to demonstrate the effectiveness and feasibility of the proposed scheme.