With the demand for wireless technology,Cognitive Radio(CR)technology is identified as a promising solution for effective spectrum utilization.Connectivity and robustness are the two main difficulties in cognitive rad...With the demand for wireless technology,Cognitive Radio(CR)technology is identified as a promising solution for effective spectrum utilization.Connectivity and robustness are the two main difficulties in cognitive radio networks due to their dynamic nature.These problems are solved by using clustering techniques which group the cognitive users into logical groups.The performance of clustering in cognitive network purely depends on cluster head selection and parameters considered for clustering.In this work,an adaptive neuro-fuzzy inference system(ANFIS)based clustering is proposed for the cognitive network.The performance of ANFIS improved using hybrid particle swarm and whale optimization algorithms for parameter tuning called PSWO.The consequent and antecedent parameters of ANFIS model are tuned by PSWO.The proper cluster heads from the network are identified using optimized ANFIS.The proposed optimized ANFIS based clustering model is analyzed in terms of number of clusters,number of common channels,reclustering rate and stability period.Simulation results indicate that proposed clustering effectively increase the stability of cluster with reduced communication overhead compared to other conventional clustering algorithms.展开更多
Interacting The highest storage capacity of a circular tank makes it pop-ular in process industries.Because of the varying surface area of the cross-sec-tions of the tank,this two-tank level system has nonlinear chara...Interacting The highest storage capacity of a circular tank makes it pop-ular in process industries.Because of the varying surface area of the cross-sec-tions of the tank,this two-tank level system has nonlinear characteristics.Controlling theflow rate of liquid is one of the most difficult challenges in the production process.This proposed effort is critical in preventing time delays and errors by managing thefluid level.Several scholars have explored and explored ways to reduce the problem of nonlinearity,but their techniques have not yielded better results.Different types of controllers with various techniques are implemented by the proposed system.Sliding Mode Controller(SMC)with Fractional Order PID Controller based on Intelligent Adaptive Neuro-Fuzzy Infer-ence System(ANFIS)is a novel technique for liquid level regulation in an inter-connected spherical tank system to avoid interferences and achieve better performance in comparison of rise time,settling time,and overshoot decrease.Evaluating the simulated results acquired by the controller yields the efficiency of the proposed system.The simulated results were produced using MATLAB 2018 and the FOMCON toolbox.Finally,the performance of the conventional controller(FOPID,PID-SMC)and proposed ANFIS based SMC-FOPID control-lers are compared and analyzed the performance indices.展开更多
文摘With the demand for wireless technology,Cognitive Radio(CR)technology is identified as a promising solution for effective spectrum utilization.Connectivity and robustness are the two main difficulties in cognitive radio networks due to their dynamic nature.These problems are solved by using clustering techniques which group the cognitive users into logical groups.The performance of clustering in cognitive network purely depends on cluster head selection and parameters considered for clustering.In this work,an adaptive neuro-fuzzy inference system(ANFIS)based clustering is proposed for the cognitive network.The performance of ANFIS improved using hybrid particle swarm and whale optimization algorithms for parameter tuning called PSWO.The consequent and antecedent parameters of ANFIS model are tuned by PSWO.The proper cluster heads from the network are identified using optimized ANFIS.The proposed optimized ANFIS based clustering model is analyzed in terms of number of clusters,number of common channels,reclustering rate and stability period.Simulation results indicate that proposed clustering effectively increase the stability of cluster with reduced communication overhead compared to other conventional clustering algorithms.
文摘Interacting The highest storage capacity of a circular tank makes it pop-ular in process industries.Because of the varying surface area of the cross-sec-tions of the tank,this two-tank level system has nonlinear characteristics.Controlling theflow rate of liquid is one of the most difficult challenges in the production process.This proposed effort is critical in preventing time delays and errors by managing thefluid level.Several scholars have explored and explored ways to reduce the problem of nonlinearity,but their techniques have not yielded better results.Different types of controllers with various techniques are implemented by the proposed system.Sliding Mode Controller(SMC)with Fractional Order PID Controller based on Intelligent Adaptive Neuro-Fuzzy Infer-ence System(ANFIS)is a novel technique for liquid level regulation in an inter-connected spherical tank system to avoid interferences and achieve better performance in comparison of rise time,settling time,and overshoot decrease.Evaluating the simulated results acquired by the controller yields the efficiency of the proposed system.The simulated results were produced using MATLAB 2018 and the FOMCON toolbox.Finally,the performance of the conventional controller(FOPID,PID-SMC)and proposed ANFIS based SMC-FOPID control-lers are compared and analyzed the performance indices.
