The motive of this work is to present a computational design using the stochastic scaled conjugate gradient(SCG)neural networks(NNs)called as SCGNNs for the socio-ecological dynamics(SED)with reef ecosystems and conse...The motive of this work is to present a computational design using the stochastic scaled conjugate gradient(SCG)neural networks(NNs)called as SCGNNs for the socio-ecological dynamics(SED)with reef ecosystems and conservation estimation.The mathematical descriptions of the SED model are provided that is dependent upon five categories,macroalgae M(v),breathing coral C(v),algal turf T(v),the density of parrotfish P(v)and the opinion of human opinion X(v).The stochastic SCGNNs process is applied to formulate the SEDmodel based on the sample statistics,testing,accreditation and training.Three different variations of the SED have been provided to authenticate the stochastic SCGNNs performance through the statics for training,accreditation,and testing are 77%,12%and 11%,respectively.The obtained numerical performances have been compared with the Runge-Kutta approach to solve the SEDmodel.The reduction of mean square error(MSE)is used to investigate the numericalmeasures through the SCGNNs for solving the SED model.The precision of the SCGNNs is validated through the comparison of the results and the absolute error performances.The reliability of the SCGNNs is performed by using the correlation values,state transitions(STs),error histograms(EHs),MSE measures and regression analysis.展开更多
The current investigations provide the solutions of the nonlinear fractional order mathematical rape and its controlmodel using the strength of artificial neural networks(ANNs)along with the Levenberg-Marquardt backpr...The current investigations provide the solutions of the nonlinear fractional order mathematical rape and its controlmodel using the strength of artificial neural networks(ANNs)along with the Levenberg-Marquardt backpropagation approach(LMBA),i.e.,artificial neural networks-Levenberg-Marquardt backpropagation approach(ANNs-LMBA).The fractional order investigations have been presented to find more realistic results of the mathematical form of the rape and its control model.The differential mathematical form of the nonlinear fractional order mathematical rape and its control model has six classes:susceptible native girls,infected immature girls,susceptible knowledgeable girls,infected knowledgeable girls,susceptible rapist population and infective rapist population.The rape and its control differential system using three different fractional order values is authenticated to perform the correctness of ANNs-LMBA.The data is used to present the rape and its control differential system is designated as 70%for training,14%for authorization and 16%for testing.The obtained performances of the ANNs-LMBA are compared with the dataset of the Adams-Bashforth-Moulton scheme.To substantiate the consistency,aptitude,validity,exactness,and capability of the LMBA neural networks,the obtained numerical values are provided using the state transitions(STs),correlation,regression,mean square error(MSE)and error histograms(EHs).展开更多
基金This project is funded by National Research Council of Thailand(NRCT)and Khon Kaen University:N42A650291。
文摘The motive of this work is to present a computational design using the stochastic scaled conjugate gradient(SCG)neural networks(NNs)called as SCGNNs for the socio-ecological dynamics(SED)with reef ecosystems and conservation estimation.The mathematical descriptions of the SED model are provided that is dependent upon five categories,macroalgae M(v),breathing coral C(v),algal turf T(v),the density of parrotfish P(v)and the opinion of human opinion X(v).The stochastic SCGNNs process is applied to formulate the SEDmodel based on the sample statistics,testing,accreditation and training.Three different variations of the SED have been provided to authenticate the stochastic SCGNNs performance through the statics for training,accreditation,and testing are 77%,12%and 11%,respectively.The obtained numerical performances have been compared with the Runge-Kutta approach to solve the SEDmodel.The reduction of mean square error(MSE)is used to investigate the numericalmeasures through the SCGNNs for solving the SED model.The precision of the SCGNNs is validated through the comparison of the results and the absolute error performances.The reliability of the SCGNNs is performed by using the correlation values,state transitions(STs),error histograms(EHs),MSE measures and regression analysis.
文摘The current investigations provide the solutions of the nonlinear fractional order mathematical rape and its controlmodel using the strength of artificial neural networks(ANNs)along with the Levenberg-Marquardt backpropagation approach(LMBA),i.e.,artificial neural networks-Levenberg-Marquardt backpropagation approach(ANNs-LMBA).The fractional order investigations have been presented to find more realistic results of the mathematical form of the rape and its control model.The differential mathematical form of the nonlinear fractional order mathematical rape and its control model has six classes:susceptible native girls,infected immature girls,susceptible knowledgeable girls,infected knowledgeable girls,susceptible rapist population and infective rapist population.The rape and its control differential system using three different fractional order values is authenticated to perform the correctness of ANNs-LMBA.The data is used to present the rape and its control differential system is designated as 70%for training,14%for authorization and 16%for testing.The obtained performances of the ANNs-LMBA are compared with the dataset of the Adams-Bashforth-Moulton scheme.To substantiate the consistency,aptitude,validity,exactness,and capability of the LMBA neural networks,the obtained numerical values are provided using the state transitions(STs),correlation,regression,mean square error(MSE)and error histograms(EHs).
