A novel higher-order context-layered recurrent pi-sigma neural network (CLRPSNN) is
presented for the identification of nonlinear dynamical systems. The proposed model is the …

In order to estimate the compressive and flexural strengths of environmentally friendly
jarosite mixed concrete, an effective prediction model based on the modified Elman neural …

This paper presents an identification model based on quantum neural network for
engineering systems. Quantum neural network (QNN) is a superior strategy to improve the …

As a practical and challenging task, deep learning-based methods have achieved effective
results for fabric defect detection, however, most of them mainly target detection accuracy at …

This paper proposes an Externally Recurrent Neural Network (ERNN) for approximating the
unknown dynamics of complex nonlinear systems and time series prediction. The proposed …

The present study emphasizes the potential use of Argon Oxygen Decarburization (AOD)
steel slag in concrete production. Five mixes along with the control mix were made by …

Fuzzy modeling is an essential tool to address uncertainties particularly in develo** a new
multi-attribute decision making (MADM) framework. A bipolar fuzzy number (BFN) is very …

Physics-informed neural networks (PINNs) have recently gained considerable attention as a
prominent deep learning technique for solving partial differential equations (PDEs) …

In recent years, deep learning has been widely applied to learning nonlinear dynamic
models for the development of a digital twin system. However, most traditional deep learning …

In this paper, a new symbolic modeling framework is proposed for the control of discrete
event systems (discrete controller synthesis). Reactive infinite-state systems are generally …