Modeling, analysis and control of multivariable fuzzy system, an important direction of intelligent techniques, are to analyze and deal with complex MIMO dynamic systems. A complex multivariable system features nonlin...Modeling, analysis and control of multivariable fuzzy system, an important direction of intelligent techniques, are to analyze and deal with complex MIMO dynamic systems. A complex multivariable system features nonlinearity, uncertainty, multiple variables and couple action. It is difficult or even impossible to effectively deal with this kind of system with the existing conventional system and control theories based on classical logic. The theory of fuzzy sets and fuzzy systems open a new alternative way to modeling, analysis and control of such systems. But most developments are limited during the dealing with SISO systems in recent years. Therefore, the study on multivariable fuzzy system is of significance in respects of theory and application, and becomes one of the focuses on the research of the fuzzy logic techniques. In this dissertation, several conclusions about the multivariable fuzzy system theory have been achieved. The whole thesis includes two parts, and the main contents and conclusions are summarized as follows: In the first part, the theory about modeling, analysis and control of multivariable fuzzy systems is studied, including 1 The study on generalized fuzzy basis function based multivariable fuzzy system model By analyzing the existing modeling methods of multivariable fuzzy systems, enlightened by the fuzzy cell to cell mapping model proposed by L.M.Jia, a new analytical description of the MIMO fuzzy rules generalized fuzzy basis function (GFBF) is put forwards under the deterministic definition of the fuzzy cellization. It cannot only simultaneously the numerical data and linguistic knowledge of the complex systems, but also contains many kinds of fuzzy basis function according to the basic properties of GFBF. Consequently, generalized fuzzy basis function series (GFBFS), an efficient and concise modeling method for MIMO fuzzy systems, is proposed through the reasonable selection for the decision making logic used in the fuzzy inference mechanism, which can be proved to approximate arbitra展开更多
In this work a Maximum Power Point Tracker (MPPT) for photovoltaic modules is developed using fuzzy logic. As it is well known, the output of the photovoltaic module is a non-linear curve which has a unique point of m...In this work a Maximum Power Point Tracker (MPPT) for photovoltaic modules is developed using fuzzy logic. As it is well known, the output of the photovoltaic module is a non-linear curve which has a unique point of maximum power (MPP) for a given condition of radiation and temperature. When a load is connected to the module, only in very specifics cases, the operation point will coincide with the MPP, for any other conditions the system will not operate with maximum power. Thus MPPT circuits must guarantee that photovoltaic modules operate with its maximum power at most of the time, independently to the radiation and temperature conditions. In order to achieve this objective, in this paper the input variables of the controller are transformed into linguistic variables, which associate with a set of rules results the displacement of the operation point so as to transfer the maximum power from the photovoltaic module to the load.展开更多
Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant rol...Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.展开更多
The performance of fuel cells and the vehicle applications they are embedded into depends on a delicate balance of the correct temperature, humidity, reactant pressure, purity and flow rate. This paper successfully in...The performance of fuel cells and the vehicle applications they are embedded into depends on a delicate balance of the correct temperature, humidity, reactant pressure, purity and flow rate. This paper successfully investigates the problem related to flow control with implementation on a single cell membrane electrode assembly （MEA）. This paper presents a systematic approach for performing system identification using recursive least squares identification to account for the non-linear parameters of the fuel cell. Then, it presents a fuzzy controller with a simplified rule base validated against real time results with the existing flow controller which calculates the flow required from the stoichiometry value.展开更多
Using rubricytes and lymphocytes as examples,this paper presents a fuzzy set theory and method to identify human bone marrow hematopoiesis system cells (BMCs).On the basis of the Cauchy’s distribution function,this p...Using rubricytes and lymphocytes as examples,this paper presents a fuzzy set theory and method to identify human bone marrow hematopoiesis system cells (BMCs).On the basis of the Cauchy’s distribution function,this paper sets up a series of membership function formulae of the BMC feature fuzzy subsets,general identification formulae of fuzzy sets for the BMCs,as well as identification formulae of fuzzy sets for rubricytes and lymphocytes.These formulae will assist with the quantitation of unknown cells compared to standard cells.展开更多
文摘Modeling, analysis and control of multivariable fuzzy system, an important direction of intelligent techniques, are to analyze and deal with complex MIMO dynamic systems. A complex multivariable system features nonlinearity, uncertainty, multiple variables and couple action. It is difficult or even impossible to effectively deal with this kind of system with the existing conventional system and control theories based on classical logic. The theory of fuzzy sets and fuzzy systems open a new alternative way to modeling, analysis and control of such systems. But most developments are limited during the dealing with SISO systems in recent years. Therefore, the study on multivariable fuzzy system is of significance in respects of theory and application, and becomes one of the focuses on the research of the fuzzy logic techniques. In this dissertation, several conclusions about the multivariable fuzzy system theory have been achieved. The whole thesis includes two parts, and the main contents and conclusions are summarized as follows: In the first part, the theory about modeling, analysis and control of multivariable fuzzy systems is studied, including 1 The study on generalized fuzzy basis function based multivariable fuzzy system model By analyzing the existing modeling methods of multivariable fuzzy systems, enlightened by the fuzzy cell to cell mapping model proposed by L.M.Jia, a new analytical description of the MIMO fuzzy rules generalized fuzzy basis function (GFBF) is put forwards under the deterministic definition of the fuzzy cellization. It cannot only simultaneously the numerical data and linguistic knowledge of the complex systems, but also contains many kinds of fuzzy basis function according to the basic properties of GFBF. Consequently, generalized fuzzy basis function series (GFBFS), an efficient and concise modeling method for MIMO fuzzy systems, is proposed through the reasonable selection for the decision making logic used in the fuzzy inference mechanism, which can be proved to approximate arbitra
文摘In this work a Maximum Power Point Tracker (MPPT) for photovoltaic modules is developed using fuzzy logic. As it is well known, the output of the photovoltaic module is a non-linear curve which has a unique point of maximum power (MPP) for a given condition of radiation and temperature. When a load is connected to the module, only in very specifics cases, the operation point will coincide with the MPP, for any other conditions the system will not operate with maximum power. Thus MPPT circuits must guarantee that photovoltaic modules operate with its maximum power at most of the time, independently to the radiation and temperature conditions. In order to achieve this objective, in this paper the input variables of the controller are transformed into linguistic variables, which associate with a set of rules results the displacement of the operation point so as to transfer the maximum power from the photovoltaic module to the load.
文摘Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.
文摘The performance of fuel cells and the vehicle applications they are embedded into depends on a delicate balance of the correct temperature, humidity, reactant pressure, purity and flow rate. This paper successfully investigates the problem related to flow control with implementation on a single cell membrane electrode assembly （MEA）. This paper presents a systematic approach for performing system identification using recursive least squares identification to account for the non-linear parameters of the fuel cell. Then, it presents a fuzzy controller with a simplified rule base validated against real time results with the existing flow controller which calculates the flow required from the stoichiometry value.
基金supported by the Science and Technology Program of Beijing Municipal Education Commission(KM201611417009)the Project of Beijing Municipal Natural Science Foundation(4142018)the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(CIT&TCD20150314)
文摘Using rubricytes and lymphocytes as examples,this paper presents a fuzzy set theory and method to identify human bone marrow hematopoiesis system cells (BMCs).On the basis of the Cauchy’s distribution function,this paper sets up a series of membership function formulae of the BMC feature fuzzy subsets,general identification formulae of fuzzy sets for the BMCs,as well as identification formulae of fuzzy sets for rubricytes and lymphocytes.These formulae will assist with the quantitation of unknown cells compared to standard cells.
