Abstract
A broad variety of real-world problems have been solved using multilayer perceptron (MLP) artificial neural networks (ANNs). Optimization techniques aid ANNs to select suitable weights and achieve correct results. Recently, the kidney-inspired algorithm (KA) has been proposed for optimization problems. This algorithm is based on the filtration, reabsorption, secretion, and excretion processes that take place in the kidneys of the human body. In the KA, the value of α in the filtration rate formula is a constant value in the range of [0, 1] that is set in the initialization stage of the algorithm. In this paper, an improved KA for optimization of the ANN model is presented in which the filtration rate is controlled by changing the value of α from minimum to maximum during the search process, which helps in achieving a better balance between exploration and exploitation in the algorithm. In this algorithm if more solutes are filtered and moved to filtered blood it means that the algorithm has more exploration. In contrast, if more solutes move to waste it means that more exploitation is performed by the algorithm. In addition, the separate use of three chaotic maps instead of a random number in the movement formula of the modified KA is investigated in order to assess the ability of each map to help to achieve superior results. The proposed method is tested on benchmark classification and time series prediction problems. The method is also applied to a real-world rainfall forecasting problem. The results of a statistical analysis prove the ability of the method.
| Original language | English |
|---|---|
| Pages (from-to) | 246-259 |
| Number of pages | 14 |
| Journal | Engineering Applications of Artificial Intelligence |
| Volume | 67 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
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Keywords
- Artificial neural network
- Chaotic map
- Classification
- Filtration rate control
- Kidney-inspired algorithm
- Real-world rainfall forecasting
- Time series prediction
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
Cite this
Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting. / Jaddi, Najmeh Sadat; Abdullah, Salwani.
In: Engineering Applications of Artificial Intelligence, Vol. 67, 01.01.2018, p. 246-259.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting
AU - Jaddi, Najmeh Sadat
AU - Abdullah, Salwani
PY - 2018/1/1
Y1 - 2018/1/1
N2 - A broad variety of real-world problems have been solved using multilayer perceptron (MLP) artificial neural networks (ANNs). Optimization techniques aid ANNs to select suitable weights and achieve correct results. Recently, the kidney-inspired algorithm (KA) has been proposed for optimization problems. This algorithm is based on the filtration, reabsorption, secretion, and excretion processes that take place in the kidneys of the human body. In the KA, the value of α in the filtration rate formula is a constant value in the range of [0, 1] that is set in the initialization stage of the algorithm. In this paper, an improved KA for optimization of the ANN model is presented in which the filtration rate is controlled by changing the value of α from minimum to maximum during the search process, which helps in achieving a better balance between exploration and exploitation in the algorithm. In this algorithm if more solutes are filtered and moved to filtered blood it means that the algorithm has more exploration. In contrast, if more solutes move to waste it means that more exploitation is performed by the algorithm. In addition, the separate use of three chaotic maps instead of a random number in the movement formula of the modified KA is investigated in order to assess the ability of each map to help to achieve superior results. The proposed method is tested on benchmark classification and time series prediction problems. The method is also applied to a real-world rainfall forecasting problem. The results of a statistical analysis prove the ability of the method.
AB - A broad variety of real-world problems have been solved using multilayer perceptron (MLP) artificial neural networks (ANNs). Optimization techniques aid ANNs to select suitable weights and achieve correct results. Recently, the kidney-inspired algorithm (KA) has been proposed for optimization problems. This algorithm is based on the filtration, reabsorption, secretion, and excretion processes that take place in the kidneys of the human body. In the KA, the value of α in the filtration rate formula is a constant value in the range of [0, 1] that is set in the initialization stage of the algorithm. In this paper, an improved KA for optimization of the ANN model is presented in which the filtration rate is controlled by changing the value of α from minimum to maximum during the search process, which helps in achieving a better balance between exploration and exploitation in the algorithm. In this algorithm if more solutes are filtered and moved to filtered blood it means that the algorithm has more exploration. In contrast, if more solutes move to waste it means that more exploitation is performed by the algorithm. In addition, the separate use of three chaotic maps instead of a random number in the movement formula of the modified KA is investigated in order to assess the ability of each map to help to achieve superior results. The proposed method is tested on benchmark classification and time series prediction problems. The method is also applied to a real-world rainfall forecasting problem. The results of a statistical analysis prove the ability of the method.
KW - Artificial neural network
KW - Chaotic map
KW - Classification
KW - Filtration rate control
KW - Kidney-inspired algorithm
KW - Real-world rainfall forecasting
KW - Time series prediction
UR - http://www.scopus.com/inward/record.url?scp=85032707160&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032707160&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2017.09.012
DO - 10.1016/j.engappai.2017.09.012
M3 - Article
AN - SCOPUS:85032707160
VL - 67
SP - 246
EP - 259
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
SN - 0952-1976
ER -