Experimental, Modeling, and Optimization Investigation on Mechanical Properties and the Crashworthiness of Thin-Walled Frusta of Silica/Epoxy Nano-composites: Fuzzy Neural Network, Particle Swarm Optimization/Multivariate Nonlinear Regression, and Gene Expression Programming

2021 ◽  
Author(s):  
A. Dadrasi ◽  
M. Shariati ◽  
Gh. A. Farzi ◽  
S. Fooladpanjeh
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Mechanical Properties ◽  
Fuzzy Neural Network ◽  
Gene Expression Programming ◽  
Nano Composites ◽  
Swarm Optimization ◽  
Multivariate Nonlinear Regression ◽  
Fuzzy Neural ◽  
Thin Walled

Fuzzy neural network and coupled gene expression programming/multivariate non-linear regression approach on mechanical features of hydroxyapatite/graphene oxide/epoxy: Empirical and optimization study

2021 ◽  
pp. 095440622110197
Author(s):  
Sasan Fooladpanjeh ◽  
Ali Dadrasi ◽  
Abdorreza Alavi Gharahbagh ◽  
Vali Parvaneh
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Mechanical Properties ◽  
Graphene Oxide ◽  
Linear Regression ◽  
Epoxy Resin ◽  
Fuzzy Neural Network ◽  
Gene Expression Programming ◽  
Non Linear ◽  
Fuzzy Neural

One way to enhance the mechanical properties of nanocomposites has been to use different fillers. In this study, ternary hybrid composites of graphene oxide/hydroxyapatite/epoxy resin were investigated. An experimental design was performed based on the central composite design (CCD). Epoxy resin was modified by incorporating different graphene oxide and hydroxyapatite weight from 0 to 0.5 wt.% and 0 to 7 wt.%, respectively. Experimental results showed that Young’s modulus, yield strength and impact strength improved up to 25.64%, 5.95% and 100.05% compared to the neat epoxy resin, respectively. In addition, gene expression programming (GEP), multivariate non-linear regression (MNLR) and fuzzy neural network (FNN) methods were employed to determine the effects of nanoparticles on the mechanical properties. Based on the modelling results, optimization process was investigated by using particle swarm optimization (PSO). Finally, the fracture surface morphologies of the nanocomposites were analyzed by scanning electron microscopy.

scholarly journals Fuzzy Neural Network Applied to Gene Expression Profiling for Predicting the Prognosis of Diffuse Large B-cell Lymphoma

2002 ◽  
Vol 93 (11) ◽  
pp. 1207-1212 ◽  
Author(s):  
Tatsuya Ando ◽  
Miyuki Suguro ◽  
Taizo Hanai ◽  
Takeshi Kobayashi ◽  
Hiroyuki Honda ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
B Cell ◽  
Expression Profiling ◽  
Fuzzy Neural Network ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
Fuzzy Neural ◽  
Large B Cell

scholarly journals Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization

2020 ◽  
Vol 10 (9) ◽  
pp. 3041
Author(s):  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Hsueh-Yi Lin ◽  
Cheng-Yi Yu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fuzzy Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Wall Following

In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.

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