H.6.2.4. Neural nets
Farnaz Sabahi
Abstract
This paper explores fixed-time synchronization for discontinuous fuzzy delay recurrent neural networks (DFRNNs) with time-varying delays. Based on a generalized variable transformation, the error system has been developed to effectively manage discontinuities in neural systems. This research addresses ...
Read More
This paper explores fixed-time synchronization for discontinuous fuzzy delay recurrent neural networks (DFRNNs) with time-varying delays. Based on a generalized variable transformation, the error system has been developed to effectively manage discontinuities in neural systems. This research addresses the fixed-time stability problem using a novel discontinuous state-feedback control input and a simple switching adaptive control scheme. The proposed method ensures robust synchronization of the drive and response neural systems within a fixed time. Practical applications of this work include improvements in protocols for secure communications, robotic control systems, and intelligent control frameworks over dynamic systems. A numerical example substantiates the theoretical claims, demonstrating the strengths of the proposed approach. The results show fixed-time convergence of error margins to zero, ensuring unbiased performance within a predefined timeframe, independent of initial conditions.
H.6.2.4. Neural nets
M. Abtahi
Abstract
This paper proposes an intelligent approach for dynamic identification of the vehicles. The proposed approach is based on the data-driven identification and uses a high-performance local model network (LMN) for estimation of the vehicle’s longitudinal velocity, lateral acceleration and yaw rate. ...
Read More
This paper proposes an intelligent approach for dynamic identification of the vehicles. The proposed approach is based on the data-driven identification and uses a high-performance local model network (LMN) for estimation of the vehicle’s longitudinal velocity, lateral acceleration and yaw rate. The proposed LMN requires no pre-defined standard vehicle model and uses measurement data to identify vehicle’s dynamics. The LMN is trained by hierarchical binary tree (HBT) learning algorithm, which results in a network with maximum generalizability and best linear or nonlinear structure. The proposed approach is applied to a measurement dataset, obtained from a Volvo V70 vehicle to estimate its longitudinal velocity, lateral acceleration and yaw rate. The results of identification revealed that the LMN can identify accurately the vehicle’s dynamics. Furthermore, comparison of LMN results and a multi-layer perceptron (MLP) neural network demonstrated the far-better performance of the proposed approach.
