In this paper, we propose a structural developmental neural network to address the plasticity‐
stability dilemma, computational inefficiency, and lack of prior knowledge in continual …

With the ongoing development and expansion of communication networks and sensors,
massive amounts of data are continuously generated in real time from real environments …

Currently, major classification methods belong to batch learning methods, which need to
obtain all data once before learning. However, in practice, it is usually difficult to handle all …

This paper proposes a hybridized forecasting method on weight adjustment of neural
networks with back-propagation learning using general type-2 fuzzy sets. Initialization of …

In the era of big data, considerable research focus is being put on designing efficient
algorithms capable of learning and extracting high-level knowledge from ubiquitous data …

An efficient classification algorithm used recently in many big data applications is the
Random forest classifier algorithm. Large complex data include patient record, medicine …

The circuitry and pathways in the brains of humans and other species have long inspired
researchers and system designers to develop accurate and efficient systems capable of …

Episodic memory enables a cognitive system to improve its performance by reflecting upon
past events. In this paper, we propose a computational model called STEM for encoding and …

Lifelong learning is still a great challenge for cognitive robots since the continuous
streaming data they encounter is usually enormous and no-stationary. Traditional cognitive …

Developmental cognitive systems can endow robots with the abilities to incrementally learn
knowledge and autonomously adapt to complex environments. Conventional cognitive …