Novel applications of intelligent computing paradigms for the analysis of nonlinear reactive transport model of the fluid in soft tissues and microvessels

This article presents a methodology to solve a one-dimensional steady-state nonlinear reactive transport model (RTM) that is meant for fluid and solute transport model of soft tissues and microvessels. The methodology integrates the artificial neural network (ANN), genetic algorithms (GAs), and patt...

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Bibliographic Details
Published in:Neural computing & applications 2019-12, Vol.31 (12), p.9041-9059
Main Authors: Ahmad, Iftikhar, Ilyas, Hira, Urooj, Aysha, Aslam, Muhammad Saeed, Shoaib, Muhammad, Raja, Muhammad Asif Zahoor
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Artificial neural networks
Boundary value problems
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Differential equations
Genetic algorithms
Image Processing and Computer Vision
Learning theory
Nonlinear analysis
Optimization
Original Article
Pattern search
Probability and Statistics in Computer Science
Soft tissues
Solvers
Transport
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Summary:This article presents a methodology to solve a one-dimensional steady-state nonlinear reactive transport model (RTM) that is meant for fluid and solute transport model of soft tissues and microvessels. The methodology integrates the artificial neural network (ANN), genetic algorithms (GAs), and pattern search (PS) aided by active-set technique (AST) and interior-point technique (IPT). The RTM is represented with nonlinear second-order system based on the boundary value problem of ordinary differential equation. The ANN modeling is used for governing expression of RTM to form a fitness function in mean square sense, and optimization solvers based on the GA, PS, GA-AST, GA-IPT, PS-AST, PS-IPT are used for viable learning of weights. Proposed techniques are applied to different nonlinear RTMs based on variation in the characteristic reaction rate and half-saturation concentration. The proposed stochastic numerical solutions are compared with state-of-the-art solvers in order to check the accuracy and convergence based on sufficient large multiple runs of the algorithms.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04203-y