Computational intelligence and neuroscience
27807444
The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostl...
In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into...
The robustness and resilience of complex networks have been widely studied and discussed in both research and industry because today, the diversity of system components and the complexity of the connection between units are increasingly influencing t...
When the state of the whole reaction network can be inferred by just measuring the dynamics of a limited set of nodes the system is said to be fully observable. However, as the number of all possible combinations of measured variables and time deriva...
IEEE transactions on neural networks and learning systems
29993752
Mean square error (MSE) is the most prominent criterion in training neural networks and has been employed in numerous learning problems. In this paper, we suggest a group of novel robust information theoretic backpropagation (BP) methods, as correntr...
This article presents a high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems. Drawing upon earlier work by Banzhaf et al. (2016), three different kinds of open-endedness are identi...
Neural networks : the official journal of the International Neural Network Society
31015043
To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is 'accum...
Neural networks : the official journal of the International Neural Network Society
31689680
Complementary Learning Systems (CLS) theory suggests that the brain uses a 'neocortical' and a 'hippocampal' learning system to achieve complex behaviour. These two systems are complementary in that the 'neocortical' system relies on slow learning of...
The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand's actuation input to the object's motion to be obtained. Without assuming tha...
Proceedings of the National Academy of Sciences of the United States of America
35759669
Understanding how the brain learns throughout a lifetime remains a long-standing challenge. In artificial neural networks (ANNs), incorporating novel information too rapidly results in catastrophic interference, i.e., abrupt loss of previously acquir...