ArticleScientific reports2025
Legendre based neural networks integrated with heuristic algorithms for the analysis of Lorenz chaotic model: an intelligent and comparative study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 citing papers in PubMed.
- Neuro-evolutionary computing approach for an epidemic model of ransomware detection using morlet wavelet neural network with meta-heuristic optimization.Scientific reports · 2026Article
- Legendre neural network-based computational study through hybrid particle swarm optimization for fractional unsteady flow of Sutterby fluid.Scientific reports · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
The optimal control methods for managing Lorenz model are achieved using an innovative intelligent computing framework that integrates artificial neural networks with stochastic unsupervised learning-based optimizers, specifically Firefly (FA) and Archimedes algorithm (AOA). The hyper parameters (weights & biases) of unsupervised neural networks are refined through Legendre polynomials Artificial Neural Networks (LENN) optimized with global search techniques, including FA-AOA collectively referred to as LENN-FA-AOA. This design approach is employed to Lorenz model across three (3) different scenarios using various step sizes and input intervals. The study's findings reveal that to minimize the computational cost to find the solution of nonlinear chaotic systems by intelligence strategies. The absolute errors values from LENN-FA-AOA with reference solution are being ranged from 3.22 × 10
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