In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame, and contain unknown functions and non-symmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but also achieves the optimal control performance. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of non-symmetric dead-zone on the control performance and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.Index Terms-Adaptive fuzzy control; reinforcement learning; nonlinear systems; optimal control; dead-zone input.1063-6706 (c)

No abstract

Quantitative features extracted from biopsy digital pathology images can provide predictive information for neoadjuvant chemoradiotherapy (nCRT) in local advanced rectal cancer (LARC) Machine learning technologies are applied to build the digital‐pathology‐based pathology signature The pathology signature is an independent predictor of treatment response to nCRT in LARC

In this paper, we aim to solve the optimal tracking control problem for the Henon Mapping chaotic system using the direct heuristic dynamic programming (DHDP) setting with filtered tracking error. The fuzzy logic system is used to approximate the long-term utility function. Compared with the results for chaotic discrete-time system, the cost of the controller is reduced. The Lyapunov analysis approach is utilized to prove the stability of the chaotic system. It is shown that the tracking error, the adaptation law and the control input retain the property of uniformly ultimate boundedness. A simulation example is given to demonstrate the effectiveness of the proposed approach.

For the security defense in the current Intelligent Transportation System (ITS), malware is often used as the security analysis data source, but only the known attack type can be detected. A general anomaly detection framework is proposed, using log data as the analysis data source. By modeling the log template sequence as a natural language sequence and using the stacked Long Short-Term Memory (LSTM) with self-attention mechanism, the framework can effectively extract the hidden pattern of the log template sequence, and well express the dependencies inside the log template sequence. The experimental results show that the overall accuracy of log sequence anomaly detection of the detection framework is better than that of existing methods and the time cost is lower.