scholarly journals Design and analysis of recurrent neural network models with non‐linear activation functions for solving time‐varying quadratic programming problems

2021 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Liangming Chen ◽  
Shuai Li ◽  
Predrag Stanimirović ◽  
Jiliang Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Quadratic Programming ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Time Varying ◽  
Activation Functions ◽  
Neural Network Models ◽  
Non Linear ◽  
Quadratic Programming Problems

Complex Neural Network Models for Time-Varying Drazin Inverse

2016 ◽  
Vol 28 (12) ◽  
pp. 2790-2824 ◽  
Author(s):  
Xue-Zhong Wang ◽  
Yimin Wei ◽  
Predrag S. Stanimirović
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convergence Analysis ◽  
Network Models ◽  
Drazin Inverse ◽  
Time Varying ◽  
Activation Functions ◽  
Neural Network Models ◽  
Complex Valued ◽  
Theoretical Results

Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arising from the limit representations of the Drazin inverse. Two types of activation functions, appropriate for handling complex matrices, are exploited to develop each of these networks. Theoretical results of convergence analysis are presented to show the desirable properties of the proposed complex-valued ZNN models. Numerical results further demonstrate the effectiveness of the proposed models.

Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data

2021 ◽  
Author(s):  
Arjun Shenoy A V ◽  
Michael A. Lones ◽  
Stephen L. Smith ◽  
Marta Vallejo
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Disease Classification ◽  
Neural Network Models

Convergence Theory for Unified Discrete-Time RNNs with Quasi-Symmetric Connection

2014 ◽  
Vol 538 ◽  
pp. 167-170
Author(s):  
Hui Zhong Mao ◽  
Chen Qiao ◽  
Wen Feng Jing ◽  
Xi Chen ◽  
Jin Qin Mao
Keyword(s):  
Neural Network ◽  
Global Convergence ◽  
Discrete Time ◽  
Recurrent Neural Network ◽  
Symmetric Matrix ◽  
Network Models ◽  
Convergence Theory ◽  
Connection Matrix ◽  
Neural Network Models ◽  
Symmetric Connection

This paper presents the global convergence theory of the discrete-time uniform pseudo projection anti-monotone network with the quasi–symmetric matrix, which removes the connection matrix constraints. The theory widens the range of applications of the discrete–time uniform pseudo projection anti–monotone network and is valid for many kinds of discrete recurrent neural network models.

scholarly journals Neural network emulation of a rainfall-runoff model

2007 ◽  
Vol 4 (1) ◽  
pp. 287-326 ◽  
Author(s):  
R. J. Abrahart ◽  
L. M. See
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Network Models ◽  
Exact Equation ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Linear Relationships ◽  
Non Linear ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a direct emulation of the Xinanjiang Rainfall-Runoff Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies have suggested that neural solutions offer no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling.

scholarly journals Extreme learning machines for weather-based modelling of silk cocoon production

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
PRAMIT PANDIT ◽  
BISHVAJIT BAKSHI ◽  
SHILPA M.
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Extreme Learning Machines ◽  
Linear Regression Models ◽  
Weather Variables ◽  
Neural Network Models ◽  
Cocoon Production ◽  
Silk Cocoon ◽  
Learning Machines ◽  
Non Linear

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear.

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