scholarly journals Selection of Important Input Parameters Using Neural Network Trained with Genetic Algorithm for Damage to Light Structures

2009 ◽  
Author(s):  
N.Y. Osman ◽  
A.W.M. Ng ◽  
K.J. McManus
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Input Parameters ◽  
Important Input ◽  
Selection Of

Bayesian and Sensitivity Analysis Approaches to Modelling the Direct Solar Irradiance

Solar Energy ◽  
2005 ◽  
Author(s):  
Philippe Lauret ◽  
Mathieu David ◽  
Eric Fock ◽  
Laetitia Adelard
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Solar Irradiance ◽  
Second Step ◽  
Practical Implementation ◽  
Bayesian Techniques ◽  
Systematic Methodology ◽  
Important Input ◽  
Input Variables ◽  
Selection Of

In this paper, emphasis is put on the design of a neural network to model the direct solar irradiance. Since unfortunately a neural network (NN) is not a statistician in-a-box, building a NN for a particular problem is a non trivial task. As a consequence, we argue that in order to properly model the direct solar irradiance, a systematic methodology must be employed. For this purpose, we propose a two-step approach to building the NN model. The first step deals with a probabilistic interpretation of the NN learning by using Bayesian techniques. The Bayesian approach to modelling offers significant advantages over the classical NN learning process. Among others, one can cite a) automatic complexity control of the NN using all the available data b) selection of the most important input variables. The second step consists in using a new sensitivity analysis-based pruning method in order to infer the optimal NN structure. We show that the combination of the two approaches makes the practical implementation of the Bayesian techniques more reliable.

scholarly journals Building stock energy modeling: Feasibility study on selection of important input parameters using stepwise regression

2020 ◽  
Author(s):  
Reza Arababadi ◽  
Hariharan Naganathan ◽  
Mohsen Saffari Pour ◽  
Atefeh Dadvar ◽  
Kristen Parrish ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Stepwise Regression ◽  
Energy Modeling ◽  
Building Stock ◽  
Input Parameters ◽  
Important Input ◽  
Selection Of

Optimizing the Fiber-to-Yarn Production Process with a Combined Neural Network/Genetic Algorithm Approach

1997 ◽  
Vol 67 (2) ◽  
pp. 84-92 ◽  
Author(s):  
S. Sette ◽  
L. Boullart ◽  
L. Van Langenhove ◽  
P. Kiekens
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Production Process ◽  
Fiber Quality ◽  
Quality Parameters ◽  
Simultaneous Optimization ◽  
Mathematical Function ◽  
Whole Process ◽  
Input Parameters ◽  
Yarn Tenacity

An important aspect of the fiber-to-yam production process is the quality of the resulting yarn. The yarn should have optimal product characteristics (and minimal faults). In theory, this objective can be realized using an optimization algorithm. The complexity of a fiber-to-yarn process is very high, however, and no mathematical function is known to exist that represents the whole process. This paper presents a method to simulate and optimize the fiber-to-yam production process using a neural network combined with a genetic algorithm. The neural network is used to model the process, with the machine settings and fiber quality parameters as input and the yarn tenacity and elongation as output. The genetic algorithm is used afterward to optimize the input parameters for obtaining the best yarns. Since this is a multi-objective optimization, the genetic algorithm is enforced with a sharing function and a Pareto optimization. The paper shows that simultaneous optimization of yarn qualities is easily achieved as a function of the necessary (optimal) input parameters, and that the results are considerably better than current manual machine intervention. The last part of the paper is dedicated to finding an optimal mixture of available fiber qualities based on the predictions of the genetic algorithm.

scholarly journals Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 396-404
Author(s):  
N Cahyani ◽  
Sinta Septi Pangastuti ◽  
K Fithriasari ◽  
Irhamah Irhamah ◽  
N Iriawan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Number ◽  
Connection Weight ◽  
The Neural Network ◽  
Network Method ◽  
Input Variables ◽  
Human Brains ◽  
Selection Of

A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.

scholarly journals Using neural network for drowsiness detection based on EEG signals and optimization in the selection of its features using genetic algorithm

2020 ◽  
Vol 8 (1) ◽  
pp. 1-9
Author(s):  
Sepehr Sarabi ◽  
Milad Asadnejad ◽  
Saman Rajabi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Accidents ◽  
Critical Factors ◽  
Eeg Signals ◽  
Statistical Population ◽  
Network Algorithms ◽  
Execution Speed ◽  
Genetic Algorithm Method ◽  
Selection Of

One of the major causes of traffic accidents is driver’s drowsiness. For this reason, detecting whether the driver's eyes are open or closed is one of the critical factors in reducing road deaths. One way to detect whether your eyes are open or closed is to use EEG signals. EEG signals are obtained from the recording of electrical activity in the human brain. The present study uses a neural network that is applied to the driver's EEG signals to detect whether the eye is open or closed. The data of the EEG signals used in this paper consist of 14 features that are based on a statistical population of 600 people. Various neural network algorithms have been implemented for clustering these data into two classes of open or closed eyes, which are described in this paper. Perceptron neural network and radial base neural network (RBF) are two types of networks used in this paper. Also, in order to improve the execution speed and reduce the occupied space of the microcontroller, the genetic algorithm method has been used to optimize the fitting function of Fisher’s discriminant rate, in which the optimized function provides better results in the less occupied time and space.

Sign in / Sign up

Export Citation Format

Share Document