Polynomial neural network-based group method of data handling algorithm coupled with modified particle swarm optimization to predict permeate flux (%) of rectangular sheet-shaped membrane

Banik, Anirban; Majumder, Mrinmoy; Biswal, Sushant Kumar; Bandyopadhyay, Tarun Kanti

doi:10.1007/s11696-021-01838-x

Cited by 3 publications

(1 citation statement)

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“…ANN is a certain abstraction and simulation of biological neural network. Over the past decade, the research of ANN, more accurately, the application research of ANN, has reached unprecedented breadth and depth [16]. However, as its unique advantages, strong learning ability, strong adaptability, and large-scale parallel processing have not been clearly shown [17].…”

Section: Methodsmentioning

confidence: 99%

Optimization of TCM Diagnosis Information Management System Based on Artificial Neural Networks

Jia

2022

Mathematical Problems in Engineering

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As a treasure of Chinese medicine, TCM has gradually formed and developed into a complete medicine with a unique medical theory system and rich treatment experience after thousands of years of medical practice. It requires high diagnostic experience, which is not conducive to application promotion and management. Therefore, the concept of digital medicine has been recognized by more and more people, in which medical diagnosis is one of the core issues of digital medicine. The accuracy and efficiency of medical diagnosis are closely related to people’s life and health, which is an important problem that cannot be ignored. Use the growing case base as knowledge base to reason and realize the diagnosis function of traditional Chinese medicine. Based on the characteristics of traditional Chinese medicine and taking case reasoning as a model, an expert system of traditional Chinese medicine diagnosis is established. This paper combines the strong learning ability, strong adaptability, and large-scale parallel processing ability of artificial neural networks (ANN) to solve the nonlinear and unstructured complex problems in management information system. By improving BP algorithm to optimize the error of weight and repair or energy parameters, the overall error of the optimized system is reduced by about 75.3% after experimental analysis, and the average accuracy of prediction is 75%.

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Section: Methodsmentioning

confidence: 99%