A Multi-Branched Radial Basis Network Approach to Predicting Complex Chaotic Behaviours

• URL: http://arxiv.org/abs/2404.00618v2
• Date: Thu, 30 May 2024 10:16:04 GMT
• Title: A Multi-Branched Radial Basis Network Approach to Predicting Complex Chaotic Behaviours
• Authors: Aarush Sinha,
• Abstract summary: We propose a multi branched network approach to predict the dynamics of a physics attractor characterized by intricate and chaotic behavior. Our results demonstrate successful prediction of the attractor's trajectory across 100 predictions made using a real-world dataset of 36,700 time-series observations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we propose a multi branched network approach to predict the dynamics of a physics attractor characterized by intricate and chaotic behavior. We introduce a unique neural network architecture comprised of Radial Basis Function (RBF) layers combined with an attention mechanism designed to effectively capture nonlinear inter-dependencies inherent in the attractor's temporal evolution. Our results demonstrate successful prediction of the attractor's trajectory across 100 predictions made using a real-world dataset of 36,700 time-series observations encompassing approximately 28 minutes of activity. To further illustrate the performance of our proposed technique, we provide comprehensive visualizations depicting the attractor's original and predicted behaviors alongside quantitative measures comparing observed versus estimated outcomes. Overall, this work showcases the potential of advanced machine learning algorithms in elucidating hidden structures in complex physical systems while offering practical applications in various domains requiring accurate short-term forecasting capabilities.

Related papers

• Dense ReLU Neural Networks for Temporal-spatial Model [13.8173644075917]
  We focus on fully connected deep neural networks utilizing the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. We derive non-asymptotic bounds that lead to convergence rates, addressing both temporal and spatial dependence in the observed measurements. We also tackle the curse of dimensionality by modeling the data on a manifold, exploring the intrinsic dimensionality of high-dimensional data.
  arXiv  Detail & Related papers  (2024-11-15T05:30:36Z)
• Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond [61.18736646013446]
  In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena.
  arXiv  Detail & Related papers  (2024-10-31T22:54:34Z)
• Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An Image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction [1.9458156037869137]
  This study proposes a novel multi-step forecasting approach by delicately integrating the PSR with neural networks to establish an end-to-end learning system. A novel deep learning model, namely PSR-GALIEN, is designed, in which the Transformer and 2D-CNN are employed for the extraction of the global and local patterns in the image. The results show that, compared with six state-of-the-art deep learning models, the forecasting performance of PSR-GALIEN consistently surpasses these baselines.
  arXiv  Detail & Related papers  (2024-07-16T09:59:13Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction [1.831835396047386]
  This study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction. Comprehensive experiments were conducted on the C-MAPSS dataset to evaluate the impact of unified versus clustering normalization.
  arXiv  Detail & Related papers  (2024-01-29T08:49:53Z)
• Deep Learning-based Analysis of Basins of Attraction [49.812879456944984]
  This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field.
  arXiv  Detail & Related papers  (2023-09-27T15:41:12Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Variational Structured Attention Networks for Deep Visual Representation Learning [49.80498066480928]
  We propose a unified deep framework to jointly learn both spatial attention maps and channel attention in a principled manner. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework. We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters.
  arXiv  Detail & Related papers  (2021-03-05T07:37:24Z)
• Learning to Abstract and Predict Human Actions [60.85905430007731]
  We model the hierarchical structure of human activities in videos and demonstrate the power of such structure in action prediction. We propose Hierarchical-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction.
  arXiv  Detail & Related papers  (2020-08-20T23:57:58Z)
• Spatio-temporal Sequence Prediction with Point Processes and Self-organizing Decision Trees [0.0]
  We study the partitioning-temporal prediction problem introduce a point-process-based prediction algorithm. Our algorithm can jointly learn the spatial event and the interaction between these regions through a gradient-based optimization procedure. We compare our approach with state-of-the-art deep learning-based approaches, where we achieve significant performance improvements.
  arXiv  Detail & Related papers  (2020-06-25T14:04:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.