Abstract: A BP neural network PID control strategy optimized based on a genetic algorithm is proposed to meet the requirements of a wide buck-boost range, low input current ripple, and a wide input ...

NeuralStockTrader/ ├── src/ │ ├── data_layer/ # Data management & feature engineering │ │ ├── data_manager.py # OHLCV data fetching, technical indicators │ │ └── feature_engineer.py # Feature ...

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. The market price of risk is taken to be λ=0. Automatic differentiation is ...

This repository provides implementation for SNBO (Scalable Neural Network-based Blackbox Optimization) — a novel method for efficient blackbox optimization using neural networks. It also includes code ...

Abstract: BP neural network has been widely used in pattern recognition, predictive analysis, control optimization, data mining, etc. Optimizing its structure holds immense importance. For the sake of ...

Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...

Species distribution models (SDMs) often overlook critical spatial heterogeneity and multiscale environmental patterns, which limit their predictive accuracy for species occurrences. We demonstrate ...

ABSTRACT: Supply chain networks, which integrate nodes such as suppliers, manufacturers, and retailers to achieve efficient coordination and allocation of resources, serve as a critical component in ...