Factored similarity models with social trust for top-N item recommendation

Trust-aware recommender systems have attracted much attention recently due to the prevalence of social networks. However, most existing trust-based approaches are designed for the recommendation task of rating prediction. Only few trust-aware methods have attempted to recommend users an ordered list of interesting items, i.e., item recommendation. In this article, we propose three factored similarity models with the incorporation of social trust for item recommendation based on implicit user feedback. Specifically, we introduce a matrix factorization technique to recover user preferences between rated items and unrated ones in the light of both user-user and item-item similarities. In addition, we claim that social trust relationships also have an important impact on a user’s preference for a specific item. Experimental results on three real-world data sets demonstrate that our approach achieves superior ranking performance to other counterparts.Accepted versio

Error-Driven-Based Nonlinear Feedback Recursive Design for Adaptive NN Trajectory Tracking Control of Surface Ships With Input Saturation

In this paper, we investigate the trajectory tracking control problem of surface ship subject to the dynamic uncertainties, unknown time-varying disturbances and input saturation. To handle the non-smooth input saturation nonlinearity and compensate the ship dynamic uncertainties, Gaussian error function and adaptive neural network technique are employed. In control design, to obtain the transient motion reference signal, finite-time nonlinear tracking differentiator is applied to generate virtual refer-ence signal and to extract the derivative of virtual control law. Referring to the effects of the kinematics subsystem on the kinetics subsystem caused by the error of tracking differentiator, and the effects of the input saturation on the control accuracy and the dynamic quality of the trajectory tracking control sys-tem, we propose an error-driven-based nonlinear feedback recursive design technique to design trajectory tracking control law, and employ a new non-quadratic Lyapunov functions to analyze the trajectory track-ing control system stability. The proposed control scheme fully embodies the characteristics of the low-gain and high-gain control, and overcomes the effect of tracking differentiator error on closed-loop system by recursive design method. Simulation results verify the effectiveness of our proposed control scheme

Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data

This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations

Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method

Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method

Anaerobic ammonium oxidation in agricultural soils : synthesis and prospective

Denitrification is considered as the dominant nitrogen (N) removing pathway, however, anaerobic oxidation of ammonium (anammox) also plays a significant part in N loss in agricultural ecosystems. Large N inputs into agricultural soils may stimulate the growth of anammox bacteria, resulting in high activity and diversity of anammox bacteria and subsequent more N loss. In some specific niches, like oxic-anoxic interface, three processes, nitrification, anammox and denitrification couple with each other, and significant anammox reaction could be observed. Soil parameters like pH, dissolved oxygen, salinity, oxidation-reduction potential (ORP), and substrate concentrations impact the anammox process. Here we summarize the current knowledge on anammox activity and contribution to N loss, abundance and diversity of anammox bacteria, factors affecting anammox, and the relationship between anammox and other N loss pathways in agricultural soils. We propose that more investigations are required for (1) the role of anammox to N loss with different agricultural management strategies; (2) microscale research on the coupling of nitrification-anammox-denitrification, that might be a very complex process but ideal model for further studies responsible for N cycling in terrestrial ecosystems; and (3) new methods to estimate differential contributions of anammox, codenitrification and denitrification in total N loss in agricultural ecosystems. New research will provide much needed information to quantify the contribution of anammox in N loss from soils at landscape, ecosystem and global scales