AIMC Topic: Learning
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PSO Algorithm-Based Design of Intelligent Education Personalization System.
Computational intelligence and neuroscience
Jul 9, 2022
The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning fi...
Image Classification Method Based on Multi-Agent Reinforcement Learning for Defects Detection for Casting.
Sensors (Basel, Switzerland)
Jul 8, 2022
A casting image classification method based on multi-agent reinforcement learning is proposed in this paper to solve the problem of casting defects detection. To reduce the detection time, each agent observes only a small part of the image and can mo...
A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys.
Scientific reports
Jul 8, 2022
We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box classification learning model that establishes a quantitative rel...
Synaptic Scaling-An Artificial Neural Network Regularization Inspired by Nature.
IEEE transactions on neural networks and learning systems
Jul 6, 2022
Nature has always inspired the human spirit and scientists frequently developed new methods based on observations from nature. Recent advances in imaging and sensing technology allow fascinating insights into biological neural processes. With the obj...
Contributions by metaplasticity to solving the Catastrophic Forgetting Problem.
Trends in neurosciences
Jul 4, 2022
Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequent...
Spatial Iterative Learning Control for Robotic Path Learning.
IEEE transactions on cybernetics
Jul 4, 2022
A spatial iterative learning control (sILC) method is proposed for a robot to learn a desired path in an unknown environment. When interacting with the environment, the robot initially starts with a predefined trajectory so an interaction force is ge...
BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis.
IEEE transactions on cybernetics
Jul 4, 2022
Sentiment analysis uses a series of automated cognitive methods to determine the author's or speaker's attitudes toward an expressed object or text's overall emotional tendencies. In recent years, the growing scale of opinionated text from social net...
Semisupervised Feature Selection via Structured Manifold Learning.
IEEE transactions on cybernetics
Jul 4, 2022
Recently, semisupervised feature selection has gained more attention in many real applications due to the high cost of obtaining labeled data. However, existing methods cannot solve the "multimodality" problem that samples in some classes lie in seve...
A Data-Driven ILC Framework for a Class of Nonlinear Discrete-Time Systems.
IEEE transactions on cybernetics
Jul 4, 2022
In this article, we propose a data-driven iterative learning control (ILC) framework for unknown nonlinear nonaffine repetitive discrete-time single-input-single-output systems by applying the dynamic linearization (DL) technique. The ILC law is cons...
Impedance Variation and Learning Strategies in Human-Robot Interaction.
IEEE transactions on cybernetics
Jul 4, 2022
In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and ma...
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