In recent years, neural networks have become a common practice in academia for handling complex problems. Numerous studies have indicated that complex problems can generally be formulated as a single or a set of time-varying equations. Dynamic neural networks, as powerful tools for processing time-varying problems, play an essential role in their online solution. This paper reviews recent advances in real-valued, complex-valued, and noise-tolerant dynamic neural networks for solving various time-varying problems, discusses the finite-time convergence, fixed/varying parameters, and noise tolerance properties of dynamic neural network models. This review is highly relevant for researchers and novices interested in using dynamic neural networks to solve time-varying problems.

The advent of artificial intelligence (AI) has catalyzed transformative changes across diverse domains, establishing itself as a disruptive technology. Traditional methods often fall short in solving intricate information engineering problems, whereas AI methods can offer superior efficiency, speed, and accuracy. This paper provides a comprehensive overview of the research and practical applications of artificial intelligence in addressing information engineering challenges over the past two decades. It focuses on four critical information engineering fields: intelligent control, pattern recognition and intelligent systems, intelligent signal processing and communication, and optimization algorithms. This paper covers the backgrounds, driving forces, technologies, and applications of these fields, as well as the applications of AI in these areas. We believe that this study will be a valuable resource for both novice and seasoned researchers interested in leveraging AI to tackle information engineering challenges, offering a comprehensive understanding of AI integration and its potential in these areas.

This paper presents an adaptive multi‐agent coordination (AMAC) strategy suitable for complex scenarios, which only requires information exchange between neighbouring robots. Unlike traditional multi‐agent coordination methods that are solved by neural dynamics, the proposed strategy displays greater flexibility, adaptability and scalability. Furthermore, the proposed AMAC strategy is reconstructed as a time‐varying complex‐valued matrix equation. By introducing a dynamic error function, a fixed‐time convergent zeroing neural network (FTCZNN) model is designed for the online solution of the AMAC strategy, with its convergence time upper bound derived theoretically. Finally, the effectiveness and applicability of the coordination control method are demonstrated by numerical simulations and physical experiments. Numerical results indicate that this method can reduce the formation error to the order of within 1.8 s.