Influenced by many complex factors, it is very difficult to obtain high-performance industrial power load forecasting. The industrial power load forecasting is deeply studied by fusing some machine-learning methods for industrial enterprise power consumers. As a result, a novel power load forecasting method is proposed by taking into account the variation of load characteristics in different regions, industries, and production patterns. First, through the improved K-means clustering analysis, the historical load data are classified as the production patterns to which they belong. Then, the prediction algorithm combining reinforcement learning with particle swarm optimization and the least-squares support vector machine is proposed. Finally, the improved algorithm in this article is used for short-term load forecasting separately by the load data in different patterns after the above processing. The forecasting method in this article is based on data driven with real datasets. The results of the simulation experiment show that the improved prediction algorithm can distinguish the changes in different production patterns and identify the load characteristics of different regions and industries with high prediction accuracy, which has practical application value.

Commercialized 5G technology will provide reliable and efficient connectivity of motor vehicles that could support the dissemination of information under an intelligent transportation system. However, such service still suffers from risks or threats due to malicious content producers. The traditional public key infrastructure (PKI) cannot restrain such untrusted but legitimate publishers. Therefore, a trust-based service management mechanism is required to secure information dissemination. The issue of how to achieve a trust management model becomes a key problem in the situation. This paper proposes a novel prototype of the decentralized trust management system (DTMS) based on blockchain technologies. Compared with the conventional and centralized trust management system, DTMS adopts a decentralized consensus-based trust evaluation model and a blockchain-based trust storage system, which provide a transparent evaluation procedure and irreversible storage of trust credits. Moreover, the proposed trust model improves blockchain efficiency by only allowing trusted nodes participating in the validation and consensus process. Additionally, the designed system creatively applies a trusted execution environment (TEE) to secure the trust evaluation process together with an incentive model that is used to stimulate more participation and penalize malicious behaviours. Finally, to evaluate our new design prototype, both numerical analysis and practical experiments are implemented for performance evaluation.

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.

Deep convolutional neural network (CNN) shows excellent effectiveness on hyperspectral image (HSI) classification. However, the architecture design of CNN requires abundant expert knowledge and experience, which poses great prohibition to its wide application in real-world engineering. To alleviate the issue, this article proposes an evolving block-based CNN (EB-CNN) to search the optimal architecture based on the genetic algorithm (GA) automatically. Specifically, two kinds of basic blocks with totally six different configurations are first designed to construct the search space. Then, a flexible encoding strategy is devised for the GA to allow different chromosomes to evolve with different lengths. In this manner, the width of each layer and the depth of the architecture can be simultaneously optimized. Furthermore, a novel swapping mutation operator is proposed for the GA to speed up the search efficiency and save computing resources. With the abovementioned techniques, the proposed algorithm automatically seeks the optimal CNN architecture for HSI classification, leading to its better usability than handcrafted CNNs. At last, extensive experiments conducted on five commonly used HSI datasets demonstrate that the proposed EB-CNN achieves highly competitive or even better performance, as compared with the state-of-the-art peer algorithms.

This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.