scholarly journals Telecare Adoption Model Based on Artificial Neural Networks

10.5772/16100 ◽  
2011 ◽  
Author(s):  
Jui-Chen Huang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Based ◽  
Adoption Model ◽  
Artificial Neural

Knowledge Sharing Adoption Model Based on Artificial Neural Networks

2012 ◽  
pp. 46-58
Author(s):  
Olusegun O. Folorunso ◽  
Rebecca Opeoluwa Vincent ◽  
Adewale Akintayo Ogunde ◽  
Benjamin Agboola
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Sharing ◽  
Technology Acceptance ◽  
Higher Learning ◽  
Perceived Usefulness ◽  
Statistical Tool ◽  
Adoption Model ◽  
Artificial Neural ◽  
Acceptance Model

Knowledge Sharing Adoption Model called (KSAM) was developed in this paper using Artificial Neural Networks (ANN). It investigated students’ Perceived Usefulness and Benefits (PUB) of Knowledge Sharing among students of higher learning in Nigeria. The study was based on the definition as well as on the constucts related to technology acceptance model (TAM). A survey was conducted using structured questionnaire administered among students and analysed with SPSS statistical tool; the results were evaluated using ANN. The KSAM includes six constucts that include Perceived Ease Of Sharing (PEOS), Perceived Usefulness and Benefits (PUB), Perceived Barriers for Sharing (PBS), External Cues to Share (ECS), Attitude Towards Sharing (ATT), and Behavioral Intention to Share (BIS). The result showed that Students’ PUB must be raised in order to effectively increase the adoption of Knowledge Sharing in this domain. The paper also identified a myriad of limitations in knowledge sharing and discovered that the utilization of KSAM using ANN is feasible. Findings from this study may form the bedrock on which further studies can be built.

Elevating Model Predictive Control Using Feedforward Artificial Neural Networks: A Review

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Senthil Kumar Arumugasamy ◽  
Zainal Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Based Control ◽  
Model Based ◽  
Non Linear ◽  
Artificial Neural ◽  
Neural Predictive Control

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

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