A Quantitative Study on Simultaneous Effects of Governing Parameters in Electrospinning of Nanofibers using Modified Neural Network using Genetic Algorithm

2017 ◽  
Vol 6 (1) ◽  
pp. 20-38
Author(s):  
Shayan Seyedin ◽  
Shima Maghsoodloo ◽  
Vahid Mottaghitalab
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Quantitative Study ◽  
Fiber Diameter ◽  
Volume Flow Rate ◽  
Solution Concentration ◽  
Electrospinning Process ◽  
Response Surfaces ◽  
Experimental Conditions

In this article, modified neural networks using genetic algorithms were employed to investigate the simultaneous effects of four of the most important parameters, namely; solution concentration (C); spinning distance (d); applied voltage (V); and volume flow rate (Q) on mean fiber diameter (MFD), as well as standard deviation of fiber diameter (StdFD) in electrospinning of polyvinyl alcohol (PVA) nanofibers. Genetic algorithm optimized neural networks (GANN) were used for modeling the electrospinning process. The results indicate better experimental conditions and more predictive ability of GANNs. Therefore, the approach of using genetic algorithms to optimize neural networks for modeling the electrospinning process has been successful. RSM could be employed when statistical analysis, quantitative study of the effects of the parameters and visualization of the response surfaces are of interest, whereas in the case of modeling the process and predicting new conditions, GANN is a more powerful tool and presents more desirable results.

scholarly journals The Use of Neural Networks and Genetic Algorithms for Design of Groundwater Remediation Schemes

1997 ◽  
Vol 1 (2) ◽  
pp. 345-356 ◽  
Author(s):  
Z. Rao ◽  
D. G. Jamieson
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Hybrid Algorithm ◽  
Large Scale ◽  
Groundwater Remediation ◽  
Injection Wells ◽  
Representative Problem

Abstract. The increasing incidence of groundwater pollution has led to recognition of a need to develop objective techniques for designing reniediation schemes. This paper outlines one such possibility for determining how many abstraction/injection wells are required, where they should be located etc., having regard to minimising the overall cost. To that end, an artificial neural network is used in association with a 2-D or 3-D groundwater simulation model to determine the performance of different combinations of abstraction/injection wells. Thereafter, a genetic algorithm is used to identify which of these combinations offers the least-cost solution to achieve the prescribed residual levels of pollutant within whatever timescale is specified. The resultant hybrid algorithm has been shown to be effective for a simplified but nevertheless representative problem; based on the results presented, it is expected the methodology developed will be equally applicable to large-scale, real-world situations.

scholarly journals Maximization of Eigenfrequency Gaps in a Composite Cylindrical Shell Using Genetic Algorithms and Neural Networks

2019 ◽  
Vol 9 (13) ◽  
pp. 2754 ◽  
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Excitation Frequency ◽  
Algorithm Optimization ◽  
External Excitation ◽  
Excitation Frequencies ◽  
Objective Function Values ◽  
Using Data ◽  
Laminated Structures

This paper presents a novel method for the maximization of eigenfrequency gaps around external excitation frequencies by stacking sequence optimization in laminated structures. The proposed procedure enables the creation of an array of suggested lamination angles to avoid resonance for each excitation frequency within the considered range. The proposed optimization algorithm, which involves genetic algorithms, artificial neural networks, and iterative retraining of the networks using data obtained from tentative optimization loops, is accurate, robust, and significantly faster than typical genetic algorithm optimization in which the objective function values are calculated using the finite element method. The combined genetic algorithm–neural network procedure was successfully applied to problems related to the avoidance of vibration resonance, which is a major concern for every structure subjected to periodic external excitations. The presented examples illustrate a combined approach to avoiding resonance through the maximization of a frequency gap around external excitation frequencies complemented by the maximization of the fundamental natural frequency. The necessary changes in natural frequencies are caused only by appropriate changes in the lamination angles. The investigated structures are thin-walled, laminated one- or three-segment shells with different boundary conditions.

An Investigation Effects of Electrospinning Parameters: Process Optimization by Application of Response Surface Methodology

2014 ◽  
Vol 660 ◽  
pp. 140-144
Author(s):  
A. Mataram ◽  
Ahmad Fauzi Ismail ◽  
A.S. Mohruni ◽  
T. Matsura
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Applied Voltage ◽  
Fiber Diameter ◽  
Solution Concentration ◽  
Response Surfaces ◽  
Nano Scale ◽  
The Mean ◽  
Nanoscale Fibers ◽  
Parameter Interactions

Effects of material and process parameters on the electrospun polyacrylonitrile fibers were experimentally investigated. Response surface methodology (RSM) was utilized to design the experiments at the setting of solution concentration, voltage and the collector distance. It also imparted the evaluation of the significance of each parameter on pore size, contact angle, modulus young and clean water permeability. Effect of applied voltage in micron-scale fiber diameter was observed to be almost negligible when solution concentration and collector distance were high. However, all three factors were found statistically significant in the production of nano-scale fibers. The response surface predictions revealed the parameter interactions for the resultant fiber diameter, and showed that there is negative correlation between the mean diameter and coefficient of variation for the fiber diameters were in agreement with the experimental results. Response surfaces were constructed to identify the processing window suitable for producing nanoscale fibers. A sub-domain of the parameter space consisting of the solution concentration, applied voltage and collector distance, was suggested for the potential nano scale fiber production.

Learning Stylistic Desires and Generating Preferred Designs of Consumers Using Neural Networks and Genetic Algorithms

2011 ◽  
Author(s):  
Ian Tseng ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Survey Data ◽  
Fuel Efficiency ◽  
Artificial Neural

Consumers have different ideas of what makes a design stylish. Some consumers may want a sporty looking car, while others may want a rugged looking or a fuel-efficient looking car. Can computers learn what it means to satisfy those style-based goals and use this knowledge to generate designs that target style-based goals in design? An experiment was conducted where participants were asked to rate computer generated car profiles for sportiness, ruggedness, beauty, and fuel efficiency. This survey data is used as an indicator of consumer stylistic form preferences, and was used to train Artificial Neural Networks (ANN) for each of the four rating categories. The resulting ANNs were then inverted using a Genetic Algorithm (GA) in order to generate new designs that elicit targeted style goals from consumers.

Effect of Electrospinning Parameters Setting towards Fiber Diameter

2013 ◽  
Vol 845 ◽  
pp. 985-988 ◽  
Author(s):  
N.H.A. Ngadiman ◽  
M.Y. Noordin ◽  
Ani Idris ◽  
Denni Kurniawan
Keyword(s):  
Flow Rate ◽  
Applied Voltage ◽  
Fiber Diameter ◽  
Volume Flow Rate ◽  
Electrospinning Process ◽  
Volume Flow ◽  
Fiber Volume ◽  
Nanofiber Diameter ◽  
New Process

Fabrication of nanofibers using electrospinning has recently attracted much attention for various applications due to its simplicity. Electrospinning has the ability to produce nanofibers within 100-500 nm. Some applications require certain fiber diameter. As a relatively new process, there are many electrospinning parameters that are believed to influence the nanofibers diameter. The purpose of this review is to identify and discuss the effect of some of those parameters, i.e. concentration, spinning distance, and applied voltage, and volume flow rate, to the nanofiber diameter during electrospinning process. It was concluded that fiber volume flow rate is proportional to fiber diameter while there is no agreement in reports on other parameters.

scholarly journals NEAT Snake: a both evolutionary and neural network adaptation approach

2020 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Vinicius Almeida dos Santos ◽  
Rodrigo Lyra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Real Time ◽  
Relative Position ◽  
Network Adaptation ◽  
The Neural Network

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.

OPTIMIZATION OF ELECTROSPINNING PROCESS PARAMETERS FOR TISSUE ENGINEERING SCAFFOLDS

2006 ◽  
Vol 01 (02) ◽  
pp. 153-178 ◽  
Author(s):  
MING CHEN ◽  
PRABIR K. PATRA ◽  
STEVEN B. WARNER ◽  
SANKHA BHOWMICK
Keyword(s):  
Cell Growth ◽  
Process Parameters ◽  
Growth Kinetics ◽  
Fiber Diameter ◽  
Average Diameter ◽  
Solution Concentration ◽  
Electrospinning Process ◽  
Average Fiber Diameter ◽  
High Concentration ◽  
Cell Growth Kinetics

The goal of the current study was to optimize important process parameters for electrospinning polycaprolactone (PCL) for growing 3T3 fibroblasts. We hypothesized that the smallest obtainable fiber diameter would provide the best cell growth kinetics and we tested this hypothesis for three different process parameters: solution concentration, voltage and collector screen distance. Beaded structures were formed when using low concentration electrospinning solutions (8 wt% to 13 wt%), in which the viscosity ranged from 16.0 c P to 340.0 c P . In this concentration range, cell growth kinetics was impeded when using a high concentration of cells (8–10 × 105). Higher PCL concentration led to an increase in the average fiber diameter from 400 nm to 1600 nm when PCL solution concentration changed from 15 wt% to 20 wt%. Although, the mean values indicated that cell growth kinetics were higher at the lower end of the concentration (15% as opposed to 20%) and this correlated with lower average fiber diameter, the results in this range were not statistically significant (p > 0.05). The average fiber diameter of scaffolds first decreased and then increased when electrospinning voltage was increased. The cell growth kinetics demonstrated that smaller average diameter PCL fiber scaffolds had higher growth kinetics than larger average diameter scaffolds with the best conditions obtained at 15 KV. By increasing the screen distance, the average fiber diameter decreased but had no significant impact on cell growth kinetics. In summary, the optimal parametric space for 3T3 fibroblast growth for our studies was electrospinning a 15 wt% PCL solution using 15 kV voltage and a 25 cm collector distance.

scholarly journals PORTFOLIO SELECTION AND MANAGEMENT USING A HYBRID INTELLIGENT AND STATISTICAL SYSTEM

2019 ◽  
pp. 31-35
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Portfolio Selection ◽  
Hybrid System ◽  
Portfolio Management ◽  
Garch Model ◽  
Statistical System ◽  
Asset Portfolio ◽  
The Garch Model

PORTFOLIO SELECTION AND MANAGEMENT USING A HYBRID INTELLIGENT AND STATISTICAL SYSTEM Juan G. Lazo Lazo, Marco Aurélio C. Pacheco, Marley Maria R. Vellasco DOI: https://doi.org/10.33017/RevECIPeru2004.0010/ ABSTRACT This paper presents the development of a hybrid system based on Genetic Algorithms, Neural Networks and the GARCH model for the selection of stocks and the management of investment portfolios. The hybrid system comprises four modules: a genetic algorithm for selecting the assets that will form the investment portfolio, the GARCH model for forecasting stock volatility, a neural network for predicting asset returns for the portfolio, and another genetic algorithm for determining the optimal weights for each asset. Portfolio management has consisted of weekly updates over a period of 49 weeks. Keywords: Genetic Algorithms, Neural Networks, GARCH, VaR, Volatility.

scholarly journals The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4683
Author(s):  
Antoni Świć ◽  
Dariusz Wołos ◽  
Arkadiusz Gola ◽  
Grzegorz Kłosowski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Short Term Memory ◽  
Real Life ◽  
Control Process ◽  
Lstm Network ◽  
High Level

The article presents an original machine-learning-based automated approach for controlling the process of machining of low-rigidity shafts using artificial intelligence methods. Three models of hybrid controllers based on different types of neural networks and genetic algorithms were developed. In this study, an objective function optimized by a genetic algorithm was replaced with a neural network trained on real-life data. The task of the genetic algorithm is to select the optimal values of the input parameters of a neural network to ensure minimum deviation. Both input vector values and the neural network’s output values are real numbers, which means the problem under consideration is regressive. The performance of three types of neural networks was analyzed: a classic multilayer perceptron network, a nonlinear autoregressive network with exogenous input (NARX) prediction network, and a deep recurrent long short-term memory (LSTM) network. Algorithmic machine learning methods were used to achieve a high level of automation of the control process. By training the network on data from real measurements, we were able to control the reliability of the turning process, taking into account many factors that are usually overlooked during mathematical modelling. Positive results of the experiments confirm the effectiveness of the proposed method for controlling low-rigidity shaft turning.

TRAINING NEURAL NETWORKS BY MEANS OF GENETIC ALGORITHMS WORKING ON VERY LONG CHROMOSOMES

1995 ◽  
Vol 06 (03) ◽  
pp. 299-316 ◽  
Author(s):  
PETER G. KORNING
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Multiple Representations ◽  
Real Life ◽  
Multilayer Perceptrons ◽  
Training Methods ◽  
Neural Net ◽  
The Neural Network

In the neural network/genetic algorithm community, rather limited success in the training of neural networks by genetic algorithms has been reported. In a paper by Whitley et al. (1991), he claims that, due to “the multiple representations problem”, genetic algorithms will not effectively be able to train multilayer perceptrons, whose chromosomal representation of its weights exceeds 300 bits. In the following paper, by use of a “real-life problem”, known to be non-trivial, and by a comparison with “classic” neural net training methods, I will try to show, that the modest success of applying genetic algorithms to the training of perceptrons, is caused not so much by the “multiple representations problem” as by the fact that problem-specific knowledge available is often ignored, thus making the problem unnecessarily tough for the genetic algorithm to solve. Special success is obtained by the use of a new fitness function, which takes into account the fact that the search performed by a genetic algorithm is holistic, and not local as is usually the case when perceptrons are trained by traditional methods.

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