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This paper studies the issue of the adaptive neural security controller design for uncertain networked singular systems in the presence of deception attacks. Considering that the attack signal is unknown, the neural networks technique is exploited to approximate the attack signal, which eliminates the assumption that the attack signal has a known upper bound. By combining the state feedback with the estimated information of the attack, the impact of the attack is effectively compensated. Furthermore, a novel Lyapunov function, including the decomposed state vector and the weight matrix estimation error, is established to evaluate the bounded area of the system state. Finally, a numerical example substantiates the validity of the theoretical results.

We propose a new variant of the Benes network using a merge-replace-fold approach. It is to realize a large port-count optical switch with low waveguide crossings. A bidirectional switch is built instead of a unidirectional switch. Compared to the classical Benes, it requires the same number of switches but far fewer intersections. The novel designs enable a 24-percentage reduction in waveguide intersections. Moreover, it has a worst-case insertion loss of 12.01 dB and a comparable crosstalk level of −13.67 dB between the neighbouring paths. Thus, it offers an energy-efficient way to solve the optical on-chip design problem in a bidirectional manner.

The proportional integral and derivative (PID) controller is extensively applied in many applications. However, three parameters must be properly adjusted to ensure effective performance of the control system: the proportional gain (KP), integral gain (KI) and derivative gain (KD). Therefore, the aim of this paper is to optimize and improve the stability, convergence and performance in autotuning the PID parameter by using a deterministic Q-SLP algorithm. The proposed method is a combination of the swarm learning process (SLP) algorithm and Q-learning algorithm. The Q-learning algorithm is applied to optimize the weight updating of the SLP algorithm based on the new deterministic rule and closed-loop stabilization of the learning rate. To validate the global optimization of the deterministic rule, it is proven based on the Bellman equation, and the stability of the learning process is proven with respect to the Lyapunov stability theorem. Additionally, to demonstrate the superiority of the performance and convergence in autotuning the PID parameter, simulation results of the proposed method are compared with those based on the central position control (CPC) system using the traditional SLP algorithm, the whale optimization algorithm (WOA) and improved particle swarm optimization (IPSO). The comparison shows that the proposed method can provide results superior to those of the other algorithms with respect to both performance indices and convergence.

Algal bloom is a nonlinear and time-varying process, which brings challenges for the accurate prediction. For the existing mechanism model of algae ignores the external key factors, we propose an algae growth model (AGM) optimized by action dependent heuristic dynamic programming (ADHDP). This model has the structure of information interaction with the outside, which can predict algal bloom with well adaptive ability. In this paper, chlorophyll-a concentration is used as the representative factor of algal bloom. We use ADHDP approach to map the external key factors to the time-varying parameters, so the AGM can be adjusted to realize the self-adaptive prediction with the changes in external environments. Compared with different prediction methods, the simulation result shows that the ADHDP-AGM prediction model can accurately predict the chlorophyll-a concentration under different data distributions. Moreover, the prediction process shows that the time-varying parameters in AGM conform to the evolution trend of chlorophyll-a concentration in fact, which further improves the interpretability of prediction model. It provides a new perspective for building a data-driven prediction model with clear physical significance, and makes the mechanism research and data science further fusion.