A survey of adaptive resonance theory neural network models for engineering applications
This survey samples from the ever-growing family of adaptive resonance theory (ART)
neural network models used to perform the three primary machine learning modalities …
neural network models used to perform the three primary machine learning modalities …
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey
Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …
availability of a historical dataset for model development, and that the resulting model will, to …
Memory efficient experience replay for streaming learning
In supervised machine learning, an agent is typically trained once and then deployed. While
this works well for static settings, robots often operate in changing environments and must …
this works well for static settings, robots often operate in changing environments and must …
Feature selection based on brain storm optimization for data classification
Brain storm optimization (BSO) is a new and effective swarm intelligence method inspired by
the human brainstorming process. This paper presents a novel BSO-based feature selection …
the human brainstorming process. This paper presents a novel BSO-based feature selection …
One or two minds? Neural network modeling of decision making by the unified self
DS Levine - Neural Networks, 2019 - Elsevier
Ever since the seminal work of Tversky and Kahneman starting in the late 1960s, it has
generally been accepted that many characteristic human decision patterns do not follow the …
generally been accepted that many characteristic human decision patterns do not follow the …
Self-organizing neural networks for universal learning and multimodal memory encoding
Learning and memory are two intertwined cognitive functions of the human brain. This paper
shows how a family of biologically-inspired self-organizing neural networks, known as fusion …
shows how a family of biologically-inspired self-organizing neural networks, known as fusion …
[HTML][HTML] Arbitrage of forecasting experts
Forecasting is an important task across several domains. Its generalised interest is related to
the uncertainty and complex evolving structure of time series. Forecasting methods are …
the uncertainty and complex evolving structure of time series. Forecasting methods are …
Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic
Currently, in executive comparative studies and even in the research studies of natural
resources, the use of maps produced by the geological survey forms the basis of geological …
resources, the use of maps produced by the geological survey forms the basis of geological …
A refined fuzzy min–max neural network with new learning procedures for pattern classification
ON Al Sayaydeh, MF Mohammed… - … on Fuzzy Systems, 2019 - ieeexplore.ieee.org
The fuzzy min-max (FMM) neural network stands as a useful model for solving pattern
classification problems. FMM has many important features, such as online learning and one …
classification problems. FMM has many important features, such as online learning and one …
[HTML][HTML] The embodied brain of SOVEREIGN2: from space-variant conscious percepts during visual search and navigation to learning invariant object categories and …
S Grossberg - Frontiers in Computational Neuroscience, 2019 - frontiersin.org
This article develops a model of how reactive and planned behaviors interact in real time.
Controllers for both animals and animats need reactive mechanisms for exploration, and …
Controllers for both animals and animats need reactive mechanisms for exploration, and …