Purpose–The purpose of this paper is to probe the potentials of computational intelligence(CI)and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to cap...Purpose–The purpose of this paper is to probe the potentials of computational intelligence(CI)and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to capture the underlying knowledge regarding a given plug-in hybrid electric vehicle’s(PHEVs)fuel cost and optimize its fuel consumption rate.Besides,the current investigation aims at elaborating the effectiveness of Pareto-based multiobjective programming for coping with the difficulties associated with such a tedious automotive engineering problem.Design/methodology/approach–The hybrid intelligent tool is implemented in two different levels.The hyper-level algorithm is a Pareto-based memetic algorithm,known as the chaos-enhanced Lamarckian immune algorithm(CLIA),with three different objective functions.As a hyper-level supervisor,CLIA tries to design a fast and accurate identifier which,at the same time,can handle the effects of uncertainty as well as use this identifier to find the optimum design parameters of PHEV for improving the fuel economy.Findings–Based on the conducted numerical simulations,a set of interesting points are inferred.First,it is observed that CI techniques provide us with a comprehensive tool capable of simultaneous identification/optimization of the PHEV operating features.It is concluded that considering fuzzy polynomial programming enables us to not only design a proper identifier but also helps us capturing the undesired effects of uncertainty and measurement noises associated with the collected database.Originality/value–To the best knowledge of the authors,this is the first attempt at implementing a comprehensive hybrid intelligent tool which can use a set of experimental data representing the behavior of PHEVs as the input and yields the optimized values of PHEV design parameters as the output.展开更多
Purpose–The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks(NNs),called multiple-valued logic neural networks(MVLNN),for predicting the behavior ...Purpose–The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks(NNs),called multiple-valued logic neural networks(MVLNN),for predicting the behavior of phenomenological systems with highly nonlinear dynamics.MVLNNs are constructed based on the integration of a number of neurons working based on the principle of multiple-valued logics.MVLNNs possess some particular features,namely complex-valued weights,input,and outputs coded by kth roots of unity,and a continuous activation as a mean for transferring numbers from complex spaces to trigonometric spaces,which distinguish them from most of the existing NNs.Design/methodology/approach–The presented study can be categorized into three sections.At the first part,the authors attempt at providing the mathematical formulations required for the implementation of ARX-based MVLNN(AMVLNN).In this context,it is indicated that how the concept of ARX can be used to revise the structure of MVLNN for online applications.Besides,the stepwise formulation for the simulation of Chua’s oscillatory map and multiple-valued logic-based BP are given.Through an analysis,some interesting characteristics of the Chua’s map,including a number of possible attractors of the state and sequences generated as a function of time,are given.Findings–Based on a throughout simulation as well as a comprehensive numerical comparative study,some important features of AMVLNN are demonstrated.The simulation results indicate that AMVLNN can be employed as a tool for the online identification of highly nonlinear dynamic systems.Furthermore,the results show the compatibility of the Chua’s oscillatory system with BP for an effective tuning of the synaptic weights.The results also unveil the potentials of AMVLNN as a fast,robust,and efficient control-oriented model at the heart of NMPC control schemes.Originality/value–This study presents two innovative propositions.First,the structure of MVLNN is modified based on the concept of ARX system identification programming to suit the base structure for coping with chaotic and highly nonlinear systems.Second,the authors share the findings about the learning characteristics of MVLNNs.Through an exhaustive comparative study and considering different rival methodologies,a novel and efficient double-stage learning strategy is proposed which remarkably improves the performance of MVLNNs.Finally,the authors describe the outline of a novel formulation which prepares the proposed AMVLNN for applications in NMPC controllers for dynamic systems.展开更多
文摘Purpose–The purpose of this paper is to probe the potentials of computational intelligence(CI)and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to capture the underlying knowledge regarding a given plug-in hybrid electric vehicle’s(PHEVs)fuel cost and optimize its fuel consumption rate.Besides,the current investigation aims at elaborating the effectiveness of Pareto-based multiobjective programming for coping with the difficulties associated with such a tedious automotive engineering problem.Design/methodology/approach–The hybrid intelligent tool is implemented in two different levels.The hyper-level algorithm is a Pareto-based memetic algorithm,known as the chaos-enhanced Lamarckian immune algorithm(CLIA),with three different objective functions.As a hyper-level supervisor,CLIA tries to design a fast and accurate identifier which,at the same time,can handle the effects of uncertainty as well as use this identifier to find the optimum design parameters of PHEV for improving the fuel economy.Findings–Based on the conducted numerical simulations,a set of interesting points are inferred.First,it is observed that CI techniques provide us with a comprehensive tool capable of simultaneous identification/optimization of the PHEV operating features.It is concluded that considering fuzzy polynomial programming enables us to not only design a proper identifier but also helps us capturing the undesired effects of uncertainty and measurement noises associated with the collected database.Originality/value–To the best knowledge of the authors,this is the first attempt at implementing a comprehensive hybrid intelligent tool which can use a set of experimental data representing the behavior of PHEVs as the input and yields the optimized values of PHEV design parameters as the output.
文摘Purpose–The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks(NNs),called multiple-valued logic neural networks(MVLNN),for predicting the behavior of phenomenological systems with highly nonlinear dynamics.MVLNNs are constructed based on the integration of a number of neurons working based on the principle of multiple-valued logics.MVLNNs possess some particular features,namely complex-valued weights,input,and outputs coded by kth roots of unity,and a continuous activation as a mean for transferring numbers from complex spaces to trigonometric spaces,which distinguish them from most of the existing NNs.Design/methodology/approach–The presented study can be categorized into three sections.At the first part,the authors attempt at providing the mathematical formulations required for the implementation of ARX-based MVLNN(AMVLNN).In this context,it is indicated that how the concept of ARX can be used to revise the structure of MVLNN for online applications.Besides,the stepwise formulation for the simulation of Chua’s oscillatory map and multiple-valued logic-based BP are given.Through an analysis,some interesting characteristics of the Chua’s map,including a number of possible attractors of the state and sequences generated as a function of time,are given.Findings–Based on a throughout simulation as well as a comprehensive numerical comparative study,some important features of AMVLNN are demonstrated.The simulation results indicate that AMVLNN can be employed as a tool for the online identification of highly nonlinear dynamic systems.Furthermore,the results show the compatibility of the Chua’s oscillatory system with BP for an effective tuning of the synaptic weights.The results also unveil the potentials of AMVLNN as a fast,robust,and efficient control-oriented model at the heart of NMPC control schemes.Originality/value–This study presents two innovative propositions.First,the structure of MVLNN is modified based on the concept of ARX system identification programming to suit the base structure for coping with chaotic and highly nonlinear systems.Second,the authors share the findings about the learning characteristics of MVLNNs.Through an exhaustive comparative study and considering different rival methodologies,a novel and efficient double-stage learning strategy is proposed which remarkably improves the performance of MVLNNs.Finally,the authors describe the outline of a novel formulation which prepares the proposed AMVLNN for applications in NMPC controllers for dynamic systems.
