ArticleComputational intelligence and neuroscience2022
Application of Intelligent Paradigm through Neural Networks for Numerical Solution of Multiorder Fractional Differential Equations.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- Research on Intelligent Sports Training System for Golf Based on Body Sense Recognition.Computational intelligence and neuroscience · 2022Article
- Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model.Applied bionics and biomechanics · 2022Article
- Effect Analysis of Positive Molecular Therapy in Surgical Nursing Based on Data Transformation Analysis.Computational and mathematical methods in medicine · 2022Article
- PSO Algorithm-Based Design of Intelligent Education Personalization System.Computational intelligence and neuroscience · 2022Article
- Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment.Computational intelligence and neuroscience · 2022Article
- Multimodal English Teaching Classroom Interaction Based on Artificial Neural Network.Computational intelligence and neuroscience · 2022Article
- Intelligent Psychology Teaching System Based on Adaptive Neural Network.Applied bionics and biomechanics · 2022Article
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5 authors.
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Abstract
In this study, the intelligent computational strength of neural networks (NNs) based on the backpropagated Levenberg-Marquardt (BLM) algorithm is utilized to investigate the numerical solution of nonlinear multiorder fractional differential equations (FDEs). The reference data set for the design of the BLM-NN algorithm for different examples of FDEs are generated by using the exact solutions. To obtain the numerical solutions, multiple operations based on training, validation, and testing on the reference data set are carried out by the design scheme for various orders of FDEs. The approximate solutions by the BLM-NN algorithm are compared with analytical solutions and performance based on mean square error (MSE), error histogram (EH), regression, and curve fitting. This further validates the accuracy, robustness, and efficiency of the proposed algorithm.
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