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

2021 ◽  
Author(s):  
Anirban Banik ◽  
Mrinmoy Majumder ◽  
Sushant Kumar Biswal ◽  
Tarun Kanti Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Group Method ◽  
Data Handling ◽  
Permeate Flux ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Polynomial Neural Network

Monopole-Gear Design Based on Neural Network and Modified Particle Swarm Optimization

2013 ◽  
Vol 477-478 ◽  
pp. 368-373 ◽  
Author(s):  
Hai Rong Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, which based on PSO algorithm, and the implementing framework of PSO and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

Soil Moisture Retrieval Using Modified Particle Swarm Optimization and Back-Propagation Neural Network

2019 ◽  
Vol 85 (11) ◽  
pp. 789-798
Author(s):  
Liangliang Tao ◽  
Guojie Wang ◽  
Xi Chen ◽  
Jing Li ◽  
Qingkong Cai
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Soil Moisture ◽  
Large Scale ◽  
Back Propagation ◽  
Particle Swarm ◽  
Optical Data ◽  
Backscatter Coefficient ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

In order to eliminate the influences of surface roughness and vegetation on radar signals in the vegetation-covered soil moisture estimation, the present paper proposes a combining method based on modified particle swarm optimization (MPSO) and back-propagation (BP) neural network algorithm. This method combines optical and radar data at the field scale and uses MPSO to optimize the weight of the neural network. An effective inertia weight is introduced in the MPSO and an implicit relationship between backscatter coefficient and soil moisture is established. Experimental results show that the combining method produces better accuracy than other inversion methods with R2 of 72.2% and Root Mean Square Error (RMSE) of 0.033 cm3/cm3, respectively. Meanwhile, the estimated accuracy of surface soil moisture using radar and optical data simultaneously is much higher than that using only a single data source as input with R2 of 0.827 and RMSE of 0.029 cm3/cm3. Therefore, the combining method can effectively improve the accuracy of soil moisture retrieval and provide support for large-scale agricultural monitoring.

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