Purpose–The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier(SONeFMUC)to classify the operating faults of a hydraulic system.The main motivati...Purpose–The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier(SONeFMUC)to classify the operating faults of a hydraulic system.The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.Design/methodology/approach–In the proposed methodology,first,the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms,i.e.a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy(CMA-ES)and a swarm-based explorer with adaptive fuzzified parameters(SBEAFP).Thereafter,a revised version of the group method data handling(GMDH)policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.Findings–Based on comparative numerical experiments,the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments.It is proved that the method outperforms some well-known classifiers such as support vector machine(SVM)and particle swarm optimization-based SVM(PSOSVM).Besides,it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier.For the case,it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities,and consequently optimize the structure of SONeFMUC.Originality/value–The originality of the paper can be considered from both numerical and practical points of view.The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults,i.e.cylinder fault,pump fault,valve leakage fault and rupture of the piping system.Besides,to elaborate on the authenticity and efficacy of the proposed method,its performance is compared with well-known rival techniques.展开更多
The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge （SOC） trajectory in plug...The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge （SOC） trajectory in plug-in hybrid electric vehicles （PHEVs）. The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categoriz- ing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm （AIA）. The objective function of the optimizer is derived from a computationally efficient artificial neural network （ANN） which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomic software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.展开更多
Purpose–The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning.Generally,by defining a proper penalty function,regularization laws are embe...Purpose–The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning.Generally,by defining a proper penalty function,regularization laws are embedded into the structure of common least square solutions to increase the numerical stability,sparsity,accuracy and robustness of regression weights.Several regularization techniques have been proposed so far which have their own advantages and disadvantages.Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques.However,the proposed numerical and deterministic approaches need certain knowledge of mathematical programming,and also do not guarantee the global optimality of the obtained solution.In this research,the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine(ELM).Design/methodology/approach–To implement the required tools for comparative numerical study,three steps are taken.The considered algorithms contain both classical and swarm and evolutionary approaches.For the classical regularization techniques,Lasso regularization,Tikhonov regularization,cascade Lasso-Tikhonov regularization,and elastic net are considered.For swarm and evolutionary-based regularization,an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered,and its algorithmic structure is modified so that it can efficiently perform the regularized learning.Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme.To test the efficacy of the proposed constraint evolutionary-based regularization technique,a wide range of regression problems are used.Besides,the proposed framework is applied to a real-life identification problem,i.e.identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine,for further assurance on the performance of the proposed scheme.Findings–Through extensive numerical study,it is observed that the proposed scheme can be easily used for regularized machine learning.It is indicated that by defining a proper objective function and considering an appropriate penalty function,near global optimum values of regressors can be easily obtained.The results attest the high potentials of swarm and evolutionary techniques for fast,accurate and robust regularized machine learning.Originality/value–The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine(OP-ELM).The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system,and also increases the degree of the automation of OP-ELM.Besides,by using different types of metaheuristics,it is demonstrated that the proposed methodology is a general flexible scheme,and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.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
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–In recent decades,development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers.This refers to the essence of real-life eng...Purpose–In recent decades,development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers.This refers to the essence of real-life engineering systems and complex natural mechanisms which are generally multi-modal,non-convex and multi-criterion.Until now,several deterministic and stochastic methods have been proposed to cope with such complex systems.Advanced soft computational methods such as evolutionary games(cooperative and non-cooperative),Pareto-based techniques,fuzzy evolutionary methods,cooperative bio-inspired algorithms and neuro-evolutionary systems have effectively come to the aid of researchers to build up efficient paradigms with application to vector optimization.The paper aims to discuss this issue.Design/methodology/approach–A novel hybrid algorithm called synchronous self-learning Pareto strategy(SSLPS)is presented for the sake of vector optimization.The method is the ensemble of evolutionary algorithms(EA),swarm intelligence(SI),adaptive version of self-organizing map(CSOM)and a data shuffling mechanism.EA are powerful numerical optimization algorithms capable of finding a global extreme point over a wide exploration domain.SI techniques(the swarm of bees in our case)can improve both intensification and robustness of exploration.CSOM network is an unsupervised learning methodology which learns the characteristics of non-dominated solutions and,thus,enhances the quality of the Pareto front.Findings–To prove the effectiveness of the proposed method,the authors engage a set of well-known benchmark functions and some well-known rival optimization methods.Additionally,SSLPS is employed for optimal design of shape memory alloy actuator as a nonlinear multi-modal real-world engineering problem.The experiments show the acceptable potential of SSLPS for handling both numerical and engineering multi-objective problems.Originality/value–To the author’s best knowledge,the proposed algorithm is among the rare multiobjective methods which fosters the use of automated unsupervised learning for increasing the intensity of Pareto front(while preserving the diversity).Also,the research evaluates the power of hybridization of SI and EA for efficient search.展开更多
The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operat...The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operation of automotive engine systems. In the recent years, a number of articles have been published which aim at the modeling and analysis of automotive engines＇ behavior during coldstart operations by using regression modeling methods. Regarding highly nonlinear and uncertain nature of the coldstart operation, calibration of the engine system＇s variables, for instance the catalyst temperature, is deemed to be an intricate task, and it is unlikely to develop an exact physics-based nonlinear model. This encourages automotive engineers to take advantage of knowledge-based modeling tools and regression approaches. However, there exist rare reports which propose an efficient tool for coping with the uncertainty associated with the collected database. Here, the authors introduce a random noise to experimentally derived data and simulate an uncertain database as a representative of the engine system＇s behavior over coldstart operations. Then, by using a Gaussian process regression machine （GPRM）, a reliable model is used for the sake of analysis of the engine＇s behavior. The simulation results attest the efficacy of GPRM for the considered case study. The research outcomes confirm that it is possible to develop a practical calibration tool which can be reliably used for modeling the catalyst temperature.展开更多
文摘Purpose–The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier(SONeFMUC)to classify the operating faults of a hydraulic system.The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.Design/methodology/approach–In the proposed methodology,first,the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms,i.e.a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy(CMA-ES)and a swarm-based explorer with adaptive fuzzified parameters(SBEAFP).Thereafter,a revised version of the group method data handling(GMDH)policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.Findings–Based on comparative numerical experiments,the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments.It is proved that the method outperforms some well-known classifiers such as support vector machine(SVM)and particle swarm optimization-based SVM(PSOSVM).Besides,it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier.For the case,it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities,and consequently optimize the structure of SONeFMUC.Originality/value–The originality of the paper can be considered from both numerical and practical points of view.The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults,i.e.cylinder fault,pump fault,valve leakage fault and rupture of the piping system.Besides,to elaborate on the authenticity and efficacy of the proposed method,its performance is compared with well-known rival techniques.
文摘The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge （SOC） trajectory in plug-in hybrid electric vehicles （PHEVs）. The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categoriz- ing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm （AIA）. The objective function of the optimizer is derived from a computationally efficient artificial neural network （ANN） which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomic software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.
文摘Purpose–The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning.Generally,by defining a proper penalty function,regularization laws are embedded into the structure of common least square solutions to increase the numerical stability,sparsity,accuracy and robustness of regression weights.Several regularization techniques have been proposed so far which have their own advantages and disadvantages.Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques.However,the proposed numerical and deterministic approaches need certain knowledge of mathematical programming,and also do not guarantee the global optimality of the obtained solution.In this research,the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine(ELM).Design/methodology/approach–To implement the required tools for comparative numerical study,three steps are taken.The considered algorithms contain both classical and swarm and evolutionary approaches.For the classical regularization techniques,Lasso regularization,Tikhonov regularization,cascade Lasso-Tikhonov regularization,and elastic net are considered.For swarm and evolutionary-based regularization,an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered,and its algorithmic structure is modified so that it can efficiently perform the regularized learning.Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme.To test the efficacy of the proposed constraint evolutionary-based regularization technique,a wide range of regression problems are used.Besides,the proposed framework is applied to a real-life identification problem,i.e.identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine,for further assurance on the performance of the proposed scheme.Findings–Through extensive numerical study,it is observed that the proposed scheme can be easily used for regularized machine learning.It is indicated that by defining a proper objective function and considering an appropriate penalty function,near global optimum values of regressors can be easily obtained.The results attest the high potentials of swarm and evolutionary techniques for fast,accurate and robust regularized machine learning.Originality/value–The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine(OP-ELM).The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system,and also increases the degree of the automation of OP-ELM.Besides,by using different types of metaheuristics,it is demonstrated that the proposed methodology is a general flexible scheme,and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.
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.
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–In recent decades,development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers.This refers to the essence of real-life engineering systems and complex natural mechanisms which are generally multi-modal,non-convex and multi-criterion.Until now,several deterministic and stochastic methods have been proposed to cope with such complex systems.Advanced soft computational methods such as evolutionary games(cooperative and non-cooperative),Pareto-based techniques,fuzzy evolutionary methods,cooperative bio-inspired algorithms and neuro-evolutionary systems have effectively come to the aid of researchers to build up efficient paradigms with application to vector optimization.The paper aims to discuss this issue.Design/methodology/approach–A novel hybrid algorithm called synchronous self-learning Pareto strategy(SSLPS)is presented for the sake of vector optimization.The method is the ensemble of evolutionary algorithms(EA),swarm intelligence(SI),adaptive version of self-organizing map(CSOM)and a data shuffling mechanism.EA are powerful numerical optimization algorithms capable of finding a global extreme point over a wide exploration domain.SI techniques(the swarm of bees in our case)can improve both intensification and robustness of exploration.CSOM network is an unsupervised learning methodology which learns the characteristics of non-dominated solutions and,thus,enhances the quality of the Pareto front.Findings–To prove the effectiveness of the proposed method,the authors engage a set of well-known benchmark functions and some well-known rival optimization methods.Additionally,SSLPS is employed for optimal design of shape memory alloy actuator as a nonlinear multi-modal real-world engineering problem.The experiments show the acceptable potential of SSLPS for handling both numerical and engineering multi-objective problems.Originality/value–To the author’s best knowledge,the proposed algorithm is among the rare multiobjective methods which fosters the use of automated unsupervised learning for increasing the intensity of Pareto front(while preserving the diversity).Also,the research evaluates the power of hybridization of SI and EA for efficient search.
文摘The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operation of automotive engine systems. In the recent years, a number of articles have been published which aim at the modeling and analysis of automotive engines＇ behavior during coldstart operations by using regression modeling methods. Regarding highly nonlinear and uncertain nature of the coldstart operation, calibration of the engine system＇s variables, for instance the catalyst temperature, is deemed to be an intricate task, and it is unlikely to develop an exact physics-based nonlinear model. This encourages automotive engineers to take advantage of knowledge-based modeling tools and regression approaches. However, there exist rare reports which propose an efficient tool for coping with the uncertainty associated with the collected database. Here, the authors introduce a random noise to experimentally derived data and simulate an uncertain database as a representative of the engine system＇s behavior over coldstart operations. Then, by using a Gaussian process regression machine （GPRM）, a reliable model is used for the sake of analysis of the engine＇s behavior. The simulation results attest the efficacy of GPRM for the considered case study. The research outcomes confirm that it is possible to develop a practical calibration tool which can be reliably used for modeling the catalyst temperature.
