Characteristics of the functional link net: a higher order delta rule net

1988 ◽  
Author(s):  
Klassen ◽  
Pao ◽  
Chen
Keyword(s):  
Higher Order ◽  
Functional Link ◽  
Delta Rule

scholarly journals A Functional Link Network With Higher Order Statistics for Signal Enhancement

2006 ◽  
Vol 54 (12) ◽  
pp. 4821-4826 ◽  
Author(s):  
Bor-Shyh Lin ◽  
Bor-Shing Lin ◽  
Fok-Ching Chong ◽  
Feipei Lai
Keyword(s):  
Order Statistics ◽  
Higher Order ◽  
Signal Enhancement ◽  
Higher Order Statistics ◽  
Functional Link

A self adaptive harmony search based functional link higher order ANN for non-linear data classification

2016 ◽  
Vol 179 ◽  
pp. 69-87 ◽  
Author(s):  
Bighnaraj Naik ◽  
Janmenjoy Nayak ◽  
H.S. Behera ◽  
Ajith Abraham
Keyword(s):  
Harmony Search ◽  
Data Classification ◽  
Higher Order ◽  
Functional Link ◽  
Non Linear ◽  
Self Adaptive

COA-HONN: cooperative optimization algorithm based higher order neural networks for stock fore casting

2019 ◽  
Vol 13 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Mohd Dilshad Ansari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Market ◽  
Optimization Algorithm ◽  
Time Budget ◽  
Statistical Significance ◽  
Higher Order ◽  
Optimization Techniques ◽  
Functional Link ◽  
Higher Order Neural Networks

A broad range of nature inspired optimization techniques are proposed and applied to forecast stock market. They performed notably differently across the stock market datasets. This article attempts to construct a cooperative optimization algorithm (COA) framework as an alternative of employing solitary algorithm. The COA considers genetic algorithm and chemical reaction optimization as constituent techniques. The framework executes each constituent algorithm with a fraction of the whole computation time budget and encourages interaction between them so that they can be benefited from each other. A component technique compares its best result so far obtained with the best established result from the other at regular interval. If the quality of the established result is better than its own best result, it replaces its solution by the received one. The COA is used to adjust the weight and bias vectors of two higher order neural networks such as Pi-Sigma neural network (PSNN) and functional link artificial neural network (FLANN) separately hence, forming two COA-HONN hybrid models. The models are evaluated on forecasting daily closing prices of five real stock datasets. The experimental results confirm that the COA approach enhances the prediction accuracy over individual algorithm. We conducted the Deibold-Mariano test to check the statistical significance of the proposed models, and it was found to be significant. Hence, the proposed approach can be used as a promising tool for stock market forecasting.

scholarly journals A Tensor Based Stacked Fuzzy Networks for Efficient Data Regression

2021 ◽  
Author(s):  
Jie Li ◽  
Jiale Hu ◽  
Guoliang Zhao ◽  
Sharina Huang ◽  
Yang Liu
Keyword(s):  
Neural Network ◽  
Fuzzy Sets ◽  
Extreme Learning Machine ◽  
Random Vector ◽  
Fuzzy Neural Network ◽  
Higher Order ◽  
Functional Link ◽  
Fuzzy Neural ◽  
Learning Machine

Abstract Random vector functional link and extreme learning machine have been extended by the type-2 fuzzy sets with vector stacked methods, this extension leads to a new way to use tensor to construct learning structure for the type-2 fuzzy sets-based learning framework. In this paper, type-2 fuzzy sets-based random vector functional link, type-2 fuzzy sets-based extreme learning machine and Tikhonov-regularized extreme learning machine are fused into one network, a tensor way of stacking data is used to incorporate the nonlinear mappings when using type-2 fuzzy sets. In this way, the network could learning the sub-structure by three sub-structures' algorithms, which are merged into one tensor structure via the type-2 fuzzy mapping results. To the stacked single fuzzy neural network, the consequent part parameters learning are implemented by unfolding tensor-based matrix regression. The newly proposed stacked single fuzzy neural network shows a new way to design the hybrid fuzzy neural network with the higher order fuzzy sets and higher order data structure. The effective of the proposed stacked single fuzzy neural network are verified by the classical testing benchmarks and several statistical testing methods.

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