A Hybrid Neural Network and Genetic Algorithm Model for Predicting Dissolved Oxygen in an Aquaculture Pond

2010 ◽  
Author(s):  
Xinying Miao ◽  
Changhui Deng ◽  
Xiangjun Li ◽  
Yanping Gao ◽  
Donggang He
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Dissolved Oxygen ◽  
Hybrid Neural Network ◽  
Aquaculture Pond ◽  
Genetic Algorithm Model

scholarly journals Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Lukas Falat ◽  
Dusan Marcek ◽  
Maria Durisova
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Soft Computing ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Moving Average ◽  
Series Data ◽  
Decision Making Process ◽  
Hybrid Neural Network

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined withK-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm

2017 ◽  
Vol 141 ◽  
pp. 19-26 ◽  
Author(s):  
Zeinab Arabasadi ◽  
Roohallah Alizadehsani ◽  
Mohamad Roshanzamir ◽  
Hossein Moosaei ◽  
Ali Asghar Yarifard
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Heart Disease ◽  
Disease Detection ◽  
Hybrid Neural Network ◽  
Computer Aided ◽  
Computer Aided Decision

scholarly journals Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

2010 ◽  
Author(s):  
Sergio Davalos ◽  
Richard Gritta ◽  
Bahram Adrangi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Local Optima ◽  
Air Carrier ◽  
Genetic Algorithm Approach ◽  
Genetic Algorithm Model

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

Sign in / Sign up

Export Citation Format

Share Document