Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Big data analytics has become a significant trend for many businesses as a result of the
daily acquisition of enormous volumes of data. This information has been gathered because …

In this technical article, to seek the optimal solution to time-dependent nonlinear optimization
subject to linear inequality and equality constraints (TDNO-IEC), the gradient-based …

Incremental random weight networks (IRWNs) face the issues of weak generalization and
complicated network structure. There is an important reason: the learning parameters of …

As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient
neural network (GNN) is recognized as an effective method for static matrix inversion with …

In this article, a distributed and time-delayed-winner-take-all (DT-WTA) network is
established and analyzed for competitively coordinated task assignment of a multirobot …

As the number of long short-term memory (LSTM) layers increases, vanishing/exploding
gradient problems exacerbate and have a negative impact on the performance of the LSTM …

Many complex nonlinear optimization or control issues can be transformed into the solving
of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and …

Abstract The World Health Organization (WHO) declared a pandemic in response to the
coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the …

It is generally considered that controlling a robot precisely becomes tough on the condition
of unknown structure information. Applying a data-driven approach to the robot control with …