scholarly journals Fusion Control of Flexible Logic Control and Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Lihua Fu ◽  
Dan Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neuron Model ◽  
Stability Control ◽  
Effective Control ◽  
Universal Logic ◽  
Pendulum System ◽  
Logic Control ◽  
Inverted Pendulum System ◽  
Artificial Neural

Based on the basic physical meaning of errorEand error varietyEC, this paper analyzes the logical relationship between them and usesUniversal Combinatorial Operation ModelinUniversal Logicto describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model ofZero-level Universal Combinatorial OperationinUniversal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.

Multiplicative neuron model artificial neural network based on Gaussian activation function

2015 ◽  
Vol 27 (4) ◽  
pp. 927-935 ◽  
Author(s):  
Ozge Gundogdu ◽  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Ufuk Yolcu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neuron Model ◽  
Activation Function ◽  
Artificial Neural

scholarly journals A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ozge Cagcag Yolcu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Fuzzy Clustering ◽  
Neuron Model ◽  
Fuzzy Relation ◽  
Optimization Techniques ◽  
Fuzzy Time Series ◽  
Target Values ◽  
Artificial Neural

Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

scholarly journals The effects of demographic characteristics and public healthcare systems on COVID-19 mortality risk in the Global South: GIS-based and artificial neural network techniques

2021 ◽  
Author(s):  
Shawky Mansour ◽  
Ammar Abulibdeh ◽  
Mohammed Alahmadi ◽  
Al Nazir Ramadan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Global Health ◽  
Mean Square Error ◽  
Mortality Rates ◽  
Global South ◽  
Effective Control ◽  
Public Healthcare ◽  
Mean Square ◽  
Artificial Neural

Abstract The coronavirus disease (COVID-19) that appeared in 2019 gave rise to a major global health crisis that is topping global health, socioeconomic and intervention programme agendas in 2020. Although the outbreak of COVID-19 has substantial and devastating impacts on developed countries, the countries of Global South share a higher proportion of the epidemic’s effects as shown particularly in morbidity and mortality rates in low-income countries . Globally, as at 13th June 2020, the total number of mortality cases was 428,337 of which 9% were in Asia (38,915) and 13.5% in South America (57,896) while 1.4% were in Africa (6080). The number of infections and deaths is still increasing rapidly at the time of writing. Modelling the effects of underlying factors and disease mortality is essential to plan effective control strategies for disease transmission and risks. The relationship between COVID-19 mortality rates and socio-demographic and health determinants can highlight various epidemic fatality risks. In this research, Geographic Information Systems (GIS) and an Artificial Neural Network (ANN) Multilayer Perceptron (MLP) were adopted to model and examine variations in COVID-19 mortality rates in the Global South. The model’s performance was tested using statistical measures of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), and the determination coefficient R2. The findings of this study indicated that the most important variable in explaining spatial mortality rate variations was the size of the elderly (65 and above) population . This was followed first by accessibility to handwashing facilities and second by hospital beds per 1000 population. Mapping the explanatory variables and estimated mortality rates and determining the importance of each variable in explaining the spatial variation of COVID-19 death rates across countries of the Global South can shed light on how public healthcare and demographic structures can offer policymakers invaluable guidelines to planning effective intervention strategies.

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