A Multi-step-Ahead Fuzzy Time Series Forecasting by Using Hybrid Chemical Reaction Optimization with Pi-Sigma Higher-Order Neural Network

2019 ◽  
pp. 1029-1041 ◽  
Author(s):  
Radha Mohan Pattanayak ◽  
H. S. Behera ◽  
Sibarama Panigrahi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Chemical Reaction ◽  
Higher Order ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Chemical Reaction Optimization ◽  
Reaction Optimization

Improving Performance of Higher Order Neural Network using Artificial Chemical Reaction Optimization

2016 ◽  
pp. 253-280 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Network ◽  
Chemical Reaction ◽  
Computational Cost ◽  
Back Propagation ◽  
Structural Complexity ◽  
Higher Order ◽  
Stock Index ◽  
Chemical Reaction Optimization ◽  
Reaction Optimization ◽  
Propagation Technique

Multilayer neural networks are commonly and frequently used technique for mapping complex nonlinear input-output relationship. However, they add more computational cost due to structural complexity in architecture. This chapter presents different functional link networks (FLN), a class of higher order neural network (HONN). FLNs are capable to handle linearly non-separable classes by increasing the dimensionality of the input space by using nonlinear combinations of input signals. Usually such network is trained with gradient descent based back propagation technique, but it suffers from many drawbacks. To overcome the drawback, here a natural chemical reaction inspired metaheuristic technique called as artificial chemical reaction optimization (ACRO) is used to train the network. As a case study, forecasting of the stock index prices of different stock markets such as BSE, NASDAQ, TAIEX, and FTSE are considered here to compare and analyze the performance gain over the traditional techniques.

Improving Performance of Higher Order Neural Network using Artificial Chemical Reaction Optimization

2016 ◽  
pp. 1753-1780
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Network ◽  
Chemical Reaction ◽  
Computational Cost ◽  
Back Propagation ◽  
Structural Complexity ◽  
Higher Order ◽  
Stock Index ◽  
Chemical Reaction Optimization ◽  
Reaction Optimization ◽  
Propagation Technique

Multilayer neural networks are commonly and frequently used technique for mapping complex nonlinear input-output relationship. However, they add more computational cost due to structural complexity in architecture. This chapter presents different functional link networks (FLN), a class of higher order neural network (HONN). FLNs are capable to handle linearly non-separable classes by increasing the dimensionality of the input space by using nonlinear combinations of input signals. Usually such network is trained with gradient descent based back propagation technique, but it suffers from many drawbacks. To overcome the drawback, here a natural chemical reaction inspired metaheuristic technique called as artificial chemical reaction optimization (ACRO) is used to train the network. As a case study, forecasting of the stock index prices of different stock markets such as BSE, NASDAQ, TAIEX, and FTSE are considered here to compare and analyze the performance gain over the traditional techniques.

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