With every year air conditioning units are increasing rapidly all over the world and air conditioning system has not only become integral part of every institution but also of our lives. The survey of this system shows that air conditioning system consumes nearly 50% of the total energy intake of a building. In air conditioning system, compressor unit consumes most of the energy. Different control techniques can be used to control the compressor speed. This book therefore provides the use of two intelligent control techniques to control the compressor speed and make air conditioning system energy efficient. The techniques used are fuzzy logic and neuro-fuzzy. This book also provides the comparison of both the techniques. This book should be helpful for professionals in artificial intelligence, or anyone else who may seek to work in Fuzzy , Neuro-fuzzy or related techniques. This book would also be helpful to students for study of these techniques.

The goal of this book is to let people know about the information retrieval system. It cover the problems in this domain and reviews the solution in current space. It explains how to build the fuzzy inference system in order to score the documents in such a way that most relevant documents will get the higher score against the user's information need. Relevant documents are ranked and then fetched on the basis on these scores. This book provides an overview of fuzzy logic and explains the core concepts underlying fuzzy logic. It also explains the design and implementation strategy of neuro fuzzy inference system for information retrieval by using Adaptive Neuro Fuzzy Inference System (ANFIS) toolbox available in MATLAB. Results and Evaluation are also given at the end for neuro fuzzy inference system and its comparison with the existing techniques for information retrieval.

Neural networks and fuzzy logic have some common features such as distributed representation of knowledge, ability to handle data with uncertainty and imprecision etc. Fuzzy logic has tolerance for imprecision of data, while neural networks have tolerance for noisy data. A neural network’s learning capability provides a good way to adjust expert’s knowledge and it automatically generates additional fuzzy rules and membership functions to meet certain specifications. This reduces the design time and cost. On the other hand, the fuzzy logic approach enhances the generalization capability of a neural network by providing more reliable output when extrapolation is needed beyond the limits of the training data. To simplify the design and optimization of fuzzy models, process learning techniques derived from neural networks (so called neuro-fuzzy approaches) can be used.Different architectures of neuro-fuzzy systems have been discussed by a number of researchers. In this research, neuro-fuzzy system networks are deployed and used for the dynamic modeling of a nonlinear MIMO system (heat recovery steam generator (HRSG)) and a permeability prediction process.

Fuzzy logic technics are not always fuzzy in technology because they are being used in electrical power stability studies to make life better. This happens because real time mathematical analysis can be substituted with real life decision making process using Fuzzy logic technics. The motivation to use fuzzy logic technics is based in the fact that many everyday technologies in the living room such as air conditioning,refridgerators, control of light and microwaves have been developed using fuzzy logic technics. Above all it has been proved by Japanese engineers that trains controlled using Fuzzy logic technics have better and smooth ride for passengers than those controlled using other methods.

This work details with the design and development of fuzzy modelling for multi variable process in distillation column. Salient Features •Fuzzy modelling of distillation column using Takagi – Sugeno model. •Design a fuzzy model predictive control (FMPC) for multi variable control of distillation column •Optimization of the system with different optimization algorithms •Comparison of the results of distillation column controlled with conventional PI controller, Model Predictive Control and FMPC. •Development of neural network model for distillation column •Design of Adaptive Neuro Fuzzy Interference System (ANFIS) for distillation column and to control the parameters. •Comparison of the results of this system controlled with conventional PI controller, Model Predictive Control and ANFIS

This book presents the details theory and applications of Fuzzy sets,fuzzy systems,membership functions & controller designed. A logic based on the two truth values True and False is sometimes inadequate when describing human reasoning. Fuzzy logic uses the whole interval between 0 (False) and 1 (True ) to describe human reasoning. As a result, fuzzy logic is being applied in rule based automatic controllers. A fuzzy control method for automatic steering and a method for line tracking are conveyed in this article. The principal for fuzzy control steering and the construction of fuzzy controller are described in detail.For Example,A vehicle is refitted with a storage battery car. Navigation control system is developed based on digital compass and other sensors. The vehicle acquires the location information using the fuzzy control steering and line tracking method. Test results indicated that the navigation lateral error was less than 0.3m when the robot ran following the predefined line route. Finally, a power system network with UPFC has been considered & A POD controller has designed by using Fuzzy Logic to improve the damping oscillation of power system network.

Over the last few decades industrial automation has become the most desirous subject to increase the productivity with quality and quantity enhancement and consider the cost management of the product. A new approach of Fuzzy Logic Time Control Discrete Event System DEV provides an opportunity to establish a control design with certain time constraint of activation under the fuzzy control of input variables. A multi-agent based approach helps the control strategy to be motivated with all internal and external factors effecting the system casually under un predetermined conditions.An approach to use the fuzzy system in local and distributed environment is explored using a simplified design algorithmic approach.Design Models of: Liquids Mixing System,Grinding and Mixing System, and Muti- Dimensional Supervisory Control System with Fuzzy Logic time control DEV strategy are explored for industrial automation.

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for several nerual netwrok and fuzzy algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed, includign elastic fuzzy control. An experimental method for determining describing function of SISO fuzzy controller is given as well. The work described in this book serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

The study done in this book was designed as a focused preliminary exploration into the power of the genetic algorithm and fuzzy logic system to the prediction of cancer survival, The results of the experiments were very positive, comparing the outcome of the GA model with that of FL it shows the robustness of the GA model as prediction system.The two principal designs indicate that the use of genetic algorithms and fuzzy logic in NPC is definitely a fruitful endeavour. The results would suggest that genetic algorithms as standalone classifier models are better (based on the system designed in this research) for this sort of task than a fuzzy logic model.

In the early phase of real-time system design only an approximate idea of the tasks and their characteristics are known. Therefore, uncertainty or impreciseness is associated with the task deadlines and processing times. It is therefore appropriate to use fuzzy numbers to model task deadlines and processing times. The book fulfills this requirement by developing several models and algorithms for fuzzy real-time scheduling using different membership function choices. It also gives task priority algorithms using a new distance measure based fuzzy criteria and considers energy efficient real-time scheduling problem with fuzzy timing constraints for the first time. This book should be of great interest to postgraduate and research students of Computer Engineering, Electronics/ Communication Engineering as well as practicing engineers using fuzzy models for designing energy efficient embedded real time systems.

The main part of the book is a comprehensive overview of the development of fuzzy logic and its applications in various areas of human affair since its genesis in the mid 1960s. This overview is then employed for assessing the significance of fuzzy logic and mathematics based on fuzzy logic.

Rough sets and Fuzzy Logic find wider applications in various fields. Considering it, the book is written to exhibit the research work of the author which will be useful to several mathematicians and computer scientists for their technical applications. In this book, the basics of fuzzy sets, fuzzy relations, fuzzy logic, the concepts of rough sets, variable precision rough sets probabilistic rough sets and rough-fuzzy hybridizations are discussed. Further, the author describes the procedure of introducing one or two thresholds in fuzzy sets through rough approximations and also describes the applications of these concepts for indexing information system with fuzzy decision attributes. Also, this book deals with a naive procedure of reducing the ambiguity in rough sets under crisp as well as fuzzy environment.

The concept of fuzzy logic is based on the fuzzy set theory which tries to represent the vagueness. Due to ambiguity, real word problems are very complex in nature. We cannot solve multifaceted vagueness using classical set theory. The reason behind that is, in classical set theory element either belongs to or does not belong to a set. Solution to real world problems can be given using human expertise and knowledge and classical logic failed to do so. Fuzzy logic provide solution to such problems as it supports to multivalued propositions. Thus fuzzy logic is widely used for application development in many areas. In this book we tried to unveil the fuzzy logic concept. We have discussed various concepts like the difference between classical set theory and fuzzy logic, fuzzy set, membership function and its type, fuzzification, fuzzy rules, method of defuzzification, etc. Then we elaborate the concept of fuzzy inference system and its implementation using MATLAB Fuzzy Logic Toolbox.

Fuzzy controllers are used to control consumer products, such as washing machines, video cameras, and rice cookers, as well as industrial processes, such as cement kilns, underground trains, and robots. Fuzzy control is a control method based on fuzzy logic. Just as Fuzzy logic can be described simply as “computing with words rather than numbers’’ and Fuzzy control can be described simply as “control with sentences rather than equations’’. A Fuzzy controller can include empirical rules, and that is especially useful in operator controlled plants.This is the world of automation. The majority of the products allow actions to be automatically triggered by events. The Performance of a SCADA system can be much improved using a fuzzy logic controller based SCADA in industries. This book describes design of fuzzy logic based SCADA Systems using MATLAB fuzzy logic toolbox.This Book is useful for Engineers and Research Scholars in the field of Electrical & Power Engineering.

## fuzzy logic and neuro fuzzy algorithms for air conditioning system в наличии / купить интернет-магазине

## Fuzzy Logic and Neuro Fuzzy Algorithms for Air Conditioning System

With every year air conditioning units are increasing rapidly all over the world and air conditioning system has not only become integral part of every institution but also of our lives. The survey of this system shows that air conditioning system consumes nearly 50% of the total energy intake of a building. In air conditioning system, compressor unit consumes most of the energy. Different control techniques can be used to control the compressor speed. This book therefore provides the use of two intelligent control techniques to control the compressor speed and make air conditioning system energy efficient. The techniques used are fuzzy logic and neuro-fuzzy. This book also provides the comparison of both the techniques. This book should be helpful for professionals in artificial intelligence, or anyone else who may seek to work in Fuzzy , Neuro-fuzzy or related techniques. This book would also be helpful to students for study of these techniques.

## Neuro Fuzzy Information Retrieval

The goal of this book is to let people know about the information retrieval system. It cover the problems in this domain and reviews the solution in current space. It explains how to build the fuzzy inference system in order to score the documents in such a way that most relevant documents will get the higher score against the user's information need. Relevant documents are ranked and then fetched on the basis on these scores. This book provides an overview of fuzzy logic and explains the core concepts underlying fuzzy logic. It also explains the design and implementation strategy of neuro fuzzy inference system for information retrieval by using Adaptive Neuro Fuzzy Inference System (ANFIS) toolbox available in MATLAB. Results and Evaluation are also given at the end for neuro fuzzy inference system and its comparison with the existing techniques for information retrieval.

## Nonlinear identification using adaptive local linear neuro-fuzzy

Neural networks and fuzzy logic have some common features such as distributed representation of knowledge, ability to handle data with uncertainty and imprecision etc. Fuzzy logic has tolerance for imprecision of data, while neural networks have tolerance for noisy data. A neural network’s learning capability provides a good way to adjust expert’s knowledge and it automatically generates additional fuzzy rules and membership functions to meet certain specifications. This reduces the design time and cost. On the other hand, the fuzzy logic approach enhances the generalization capability of a neural network by providing more reliable output when extrapolation is needed beyond the limits of the training data. To simplify the design and optimization of fuzzy models, process learning techniques derived from neural networks (so called neuro-fuzzy approaches) can be used.Different architectures of neuro-fuzzy systems have been discussed by a number of researchers. In this research, neuro-fuzzy system networks are deployed and used for the dynamic modeling of a nonlinear MIMO system (heat recovery steam generator (HRSG)) and a permeability prediction process.

## Fuzzy Dynamic Load Analysis and Power System Voltage Stability Studies

Fuzzy logic technics are not always fuzzy in technology because they are being used in electrical power stability studies to make life better. This happens because real time mathematical analysis can be substituted with real life decision making process using Fuzzy logic technics. The motivation to use fuzzy logic technics is based in the fact that many everyday technologies in the living room such as air conditioning,refridgerators, control of light and microwaves have been developed using fuzzy logic technics. Above all it has been proved by Japanese engineers that trains controlled using Fuzzy logic technics have better and smooth ride for passengers than those controlled using other methods.

## Design and Development of Fuzzy Controllers for MIMO Systems

This work details with the design and development of fuzzy modelling for multi variable process in distillation column. Salient Features •Fuzzy modelling of distillation column using Takagi – Sugeno model. •Design a fuzzy model predictive control (FMPC) for multi variable control of distillation column •Optimization of the system with different optimization algorithms •Comparison of the results of distillation column controlled with conventional PI controller, Model Predictive Control and FMPC. •Development of neural network model for distillation column •Design of Adaptive Neuro Fuzzy Interference System (ANFIS) for distillation column and to control the parameters. •Comparison of the results of this system controlled with conventional PI controller, Model Predictive Control and ANFIS

## Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration,

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration,

## Fuzzy Logic Controller

This book presents the details theory and applications of Fuzzy sets,fuzzy systems,membership functions & controller designed. A logic based on the two truth values True and False is sometimes inadequate when describing human reasoning. Fuzzy logic uses the whole interval between 0 (False) and 1 (True ) to describe human reasoning. As a result, fuzzy logic is being applied in rule based automatic controllers. A fuzzy control method for automatic steering and a method for line tracking are conveyed in this article. The principal for fuzzy control steering and the construction of fuzzy controller are described in detail.For Example,A vehicle is refitted with a storage battery car. Navigation control system is developed based on digital compass and other sensors. The vehicle acquires the location information using the fuzzy control steering and line tracking method. Test results indicated that the navigation lateral error was less than 0.3m when the robot ran following the predefined line route. Finally, a power system network with UPFC has been considered & A POD controller has designed by using Fuzzy Logic to improve the damping oscillation of power system network.

## Applications of Fuzzy Logic Time Control in Industrial Automation

Over the last few decades industrial automation has become the most desirous subject to increase the productivity with quality and quantity enhancement and consider the cost management of the product. A new approach of Fuzzy Logic Time Control Discrete Event System DEV provides an opportunity to establish a control design with certain time constraint of activation under the fuzzy control of input variables. A multi-agent based approach helps the control strategy to be motivated with all internal and external factors effecting the system casually under un predetermined conditions.An approach to use the fuzzy system in local and distributed environment is explored using a simplified design algorithmic approach.Design Models of: Liquids Mixing System,Grinding and Mixing System, and Muti- Dimensional Supervisory Control System with Fuzzy Logic time control DEV strategy are explored for industrial automation.

## Intelligent control of industrial and power systems

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for several nerual netwrok and fuzzy algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed, includign elastic fuzzy control. An experimental method for determining describing function of SISO fuzzy controller is given as well. The work described in this book serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

## Applications of Soft Computing Techniques for Cancer Prognosis

The study done in this book was designed as a focused preliminary exploration into the power of the genetic algorithm and fuzzy logic system to the prediction of cancer survival, The results of the experiments were very positive, comparing the outcome of the GA model with that of FL it shows the robustness of the GA model as prediction system.The two principal designs indicate that the use of genetic algorithms and fuzzy logic in NPC is definitely a fruitful endeavour. The results would suggest that genetic algorithms as standalone classifier models are better (based on the system designed in this research) for this sort of task than a fuzzy logic model.

## FUZZY UNCERTAINTY MODELS AND ALGORITHMS

In the early phase of real-time system design only an approximate idea of the tasks and their characteristics are known. Therefore, uncertainty or impreciseness is associated with the task deadlines and processing times. It is therefore appropriate to use fuzzy numbers to model task deadlines and processing times. The book fulfills this requirement by developing several models and algorithms for fuzzy real-time scheduling using different membership function choices. It also gives task priority algorithms using a new distance measure based fuzzy criteria and considers energy efficient real-time scheduling problem with fuzzy timing constraints for the first time. This book should be of great interest to postgraduate and research students of Computer Engineering, Electronics/ Communication Engineering as well as practicing engineers using fuzzy models for designing energy efficient embedded real time systems.

## Fuzzy Logic and Mathematics

The main part of the book is a comprehensive overview of the development of fuzzy logic and its applications in various areas of human affair since its genesis in the mid 1960s. This overview is then employed for assessing the significance of fuzzy logic and mathematics based on fuzzy logic.

## Algebra of Information Systems using Rough Sets and Fuzzy Sets

Rough sets and Fuzzy Logic find wider applications in various fields. Considering it, the book is written to exhibit the research work of the author which will be useful to several mathematicians and computer scientists for their technical applications. In this book, the basics of fuzzy sets, fuzzy relations, fuzzy logic, the concepts of rough sets, variable precision rough sets probabilistic rough sets and rough-fuzzy hybridizations are discussed. Further, the author describes the procedure of introducing one or two thresholds in fuzzy sets through rough approximations and also describes the applications of these concepts for indexing information system with fuzzy decision attributes. Also, this book deals with a naive procedure of reducing the ambiguity in rough sets under crisp as well as fuzzy environment.

## Fuzzy Logic Unveiled

The concept of fuzzy logic is based on the fuzzy set theory which tries to represent the vagueness. Due to ambiguity, real word problems are very complex in nature. We cannot solve multifaceted vagueness using classical set theory. The reason behind that is, in classical set theory element either belongs to or does not belong to a set. Solution to real world problems can be given using human expertise and knowledge and classical logic failed to do so. Fuzzy logic provide solution to such problems as it supports to multivalued propositions. Thus fuzzy logic is widely used for application development in many areas. In this book we tried to unveil the fuzzy logic concept. We have discussed various concepts like the difference between classical set theory and fuzzy logic, fuzzy set, membership function and its type, fuzzification, fuzzy rules, method of defuzzification, etc. Then we elaborate the concept of fuzzy inference system and its implementation using MATLAB Fuzzy Logic Toolbox.

## Design of Fuzzy Logic Based SCADA System

Fuzzy controllers are used to control consumer products, such as washing machines, video cameras, and rice cookers, as well as industrial processes, such as cement kilns, underground trains, and robots. Fuzzy control is a control method based on fuzzy logic. Just as Fuzzy logic can be described simply as “computing with words rather than numbers’’ and Fuzzy control can be described simply as “control with sentences rather than equations’’. A Fuzzy controller can include empirical rules, and that is especially useful in operator controlled plants.This is the world of automation. The majority of the products allow actions to be automatically triggered by events. The Performance of a SCADA system can be much improved using a fuzzy logic controller based SCADA in industries. This book describes design of fuzzy logic based SCADA Systems using MATLAB fuzzy logic toolbox.This Book is useful for Engineers and Research Scholars in the field of Electrical & Power Engineering.