Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network

• URL: http://arxiv.org/abs/2402.18576v1
• Date: Wed, 10 Jan 2024 01:15:33 GMT
• Title: Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network
• Abstract summary: This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight technique. The IW technique, inspired by the motions of a golf ball, modified the particles' velocities as they approached the solution point to a parabolically descending structure. Applying the modified ANN-PSO using RDV IW technique greatly improved the new HIV/AIDS forecasting model compared with the two models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision making and planning have long relied heavily on AI-driven forecasts. The government and the general public are working to minimize the risks while maximizing benefits in the face of potential future public health uncertainties. This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight (RDV IW) technique to improve the convergence of Particle Swarm Optimization (PSO) and the accuracy of Artificial Neural Network (ANN). The IW technique, inspired by the motions of a golf ball, modified the particles' velocities as they approached the solution point to a parabolically descending structure. Simulation results revealed that the proposed forecasting model with [0.4, 0.9] combination of alpha and alpha_dump exhibits a 6.36% improvement in position error and 11.75% improvement in computational time compared to the old model, thus, improving its convergence. It reached the optimum level at minimal steps with 12.50% improvement as against the old model since it provides better velocity averages when speed stabilization occurs at the 24th iteration. Meanwhile, the computed p-values for NRMSE (0.04889174), MAE (0.02829063), MAPE (0.02226053), WAPE (0.01701545), and R2 (0.00000021) of the proposed algorithm are less than the set 0.05 level of significance, thus the values indicated a significant result in terms of accuracy performance. Applying the modified ANN-PSO using RDV IW technique greatly improved the new HIV/AIDS forecasting model compared with the two models.

Related papers

• Scaling Reinforcement Learning for Diffusion Models via Velocity Matching [66.34541945134916]
  RewardBench fine-tuning is an important tool for adapting diffusion models to human preferences and task-specific objectives.<n>Unlike autoregressive models, diffusion models do not provide tractable likelihoods for generated samples.<n>We propose reward-based velocity matching (RVM), a simple trajectory-free update that acts directly on the velocity field.
  arXiv  Detail & Related papers  (2026-08-24T17:13:43Z)
• Enhanced Artificial Neural Networks Using QHAdamW in Air Quality Forecasting [0.0]
  The study employed an Artificial Neural Network in combination with the optimized Adaptive Moment Estimation (Adam) algorithm.<n>Based on data from a real-time air quality tracking station in Manila, a feed-forward neural network is used to predict the AQI of PM2.5 and PM10 separately.
  arXiv  Detail & Related papers  (2026-08-20T23:59:41Z)
• Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting [30.958839430506156]
  Otter Weather is a highly efficient forecasting model designed to democratise high-performance weather prediction with AI.<n>The version outperforms the best NWP baseline by 9.6% at a 24-hour lead time.<n>Otter-XL achieves a 9.7% improvement over the IFS ENS baseline.
  arXiv  Detail & Related papers  (2026-06-24T22:23:13Z)
• NOVAK: Unified adaptive optimizer for deep neural networks [0.0]
  NOVAK is a gradient-based optimization algorithm that integrates adaptive moment estimation, rectified learning-rate scheduling, decoupled weight regularization, multiple variants of Nesterov momentum, and lookahead synchronization into a unified, performance-oriented framework.
  arXiv  Detail & Related papers  (2026-01-11T13:03:57Z)
• Enhancing Analogy-Based Software Effort Estimation with Firefly Algorithm Optimization [0.0]
  This study proposes a Firefly Algorithm-guided Analogy-Based Estimation (FAABE) model that combines FA with ABE to improve estimation accuracy.<n>Compared to conventional models, the experimental results show notable increases in prediction precision.
  arXiv  Detail & Related papers  (2025-11-29T17:56:51Z)
• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• Online high-precision prediction method for injection molding product weight by integrating time series/non-time series mixed features and feature attention mechanism [3.5881814709064934]
  This study proposes a mixed feature attention-artificial neural network (MFA-ANN) model for high-precision online prediction of product weight.<n>Results demonstrate that the MFA-ANN model achieves a RMSE of 0.0281 with 0.5 g weight fluctuation tolerance, outperforming conventional benchmarks.
  arXiv  Detail & Related papers  (2025-06-23T08:40:50Z)
• LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models [76.8317443926908]
  Masked Diffusion Models (MDMs) present a promising paradigm for language modeling.<n>The challenge arises from the high variance in Evidence Lower Bound (ELBO)-based likelihood estimates required for preference optimization.<n>We propose Variance-Reduced Preference Optimization (VRPO), a framework that formally analyzes the variance of ELBO estimators and derives on both the bias and variance of preference optimization gradients.
  arXiv  Detail & Related papers  (2025-05-25T16:36:20Z)
• How to systematically develop an effective AI-based bias correction model? [15.73933701556121]
  This study introduces ReSA-ConvLSTM, an artificial intelligence (AI) framework for systematic bias correction in numerical weather prediction (NWP) We propose three innovations by integrating dynamic climatological normalization, ConvLSTM with temporal causality constraints, and residual self-attention mechanisms. Using 41 years (1981-2021) of global atmospheric data, the framework reduces systematic biases in 2-m air temperature (T2m), 10-m winds (UV10), and sea-level pressure (SLP) The lightweight architecture (10.6M parameters) enables efficient generalization to multiple variables and downstream applications, reducing retraining time by
  arXiv  Detail & Related papers  (2025-04-21T03:02:42Z)
• Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction [0.0]
  We propose a new approach to build efficient and accurate predictive models for aerodynamic performance prediction. To clearly describe the interplay between design variables, hierarchical and categorical kernels are used in the BO formulation. For the drag coefficient prediction task, the Mean Absolute Percentage Error (MAPE) of our optimized model drops from 0.1433% to 0.0163%. Our model achieves a MAPE of 0.82% on a benchmark aircraft self-noise prediction problem, significantly outperforming existing models.
  arXiv  Detail & Related papers  (2025-03-25T09:14:36Z)
• MARS: Unleashing the Power of Variance Reduction for Training Large Models [56.47014540413659]
  Large gradient algorithms like Adam, Adam, and their variants have been central to the development of this type of training. We propose a framework that reconciles preconditioned gradient optimization methods with variance reduction via a scaled momentum technique.
  arXiv  Detail & Related papers  (2024-11-15T18:57:39Z)
• Robust Fine-tuning of Zero-shot Models via Variance Reduction [56.360865951192324]
  When fine-tuning zero-shot models, our desideratum is for the fine-tuned model to excel in both in-distribution (ID) and out-of-distribution (OOD) We propose a sample-wise ensembling technique that can simultaneously attain the best ID and OOD accuracy without the trade-offs.
  arXiv  Detail & Related papers  (2024-11-11T13:13:39Z)
• Don't You (Project Around Discs)? Neural Network Surrogate and Projected Gradient Descent for Calibrating an Intervertebral Disc Finite Element Model [9.456474817418703]
  This study introduces a novel, efficient, and effective calibration method for an L4-L5 IVD FE model using a neural network (NN) surrogate. The NN surrogate predicts simulation outcomes with high accuracy, outperforming other machine learning models. Our approach reduces calibration time to under three seconds, compared to up to 8 days per sample required by traditional calibration.
  arXiv  Detail & Related papers  (2024-08-12T11:39:21Z)
• Comparative Evaluation of Metaheuristic Algorithms for Hyperparameter Selection in Short-Term Weather Forecasting [0.0]
  This paper explores the application of metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) We evaluate their performance in weather forecasting based on metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE)
  arXiv  Detail & Related papers  (2023-09-05T22:13:35Z)
• An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection [3.3398969693904723]
  We propose an improved lightweight face mask detector based on YOLOv5. It achieves a mean average precision of 95.2%, which is 4.4% higher than the baseline and is also more accurate compared with other existing models.
  arXiv  Detail & Related papers  (2022-03-30T17:41:21Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• Non-Parametric Adaptive Network Pruning [125.4414216272874]
  We introduce non-parametric modeling to simplify the algorithm design. Inspired by the face recognition community, we use a message passing algorithm to obtain an adaptive number of exemplars. EPruner breaks the dependency on the training data in determining the "important" filters.
  arXiv  Detail & Related papers  (2021-01-20T06:18:38Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)
• Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN [0.0]
  Six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM) The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization.
  arXiv  Detail & Related papers  (2020-06-28T11:06:53Z)
• Improved Adversarial Training via Learned Optimizer [101.38877975769198]
  We propose a framework to improve the robustness of adversarial training models. By co-training's parameters model's weights, the proposed framework consistently improves robustness and steps adaptively for update directions.
  arXiv  Detail & Related papers  (2020-04-25T20:15:53Z)

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.