The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic, mathematical modelling of electrical machines, power systems control and VHDL design. Unlike the academic monographs that have previously been published on each of these subjects, this book combines them and is based round case studies of systems analysis, control strategies, design, simulation and implementation. The result is a guide to applied control systems design that will appeal equally to students and professional design engineers. The book can also be used as a unique VHDL design aid, based on real-world power engineering applications.*Introduces cutting-edge control systems to a wide readership of engineers and students*The first book on neuro-fuzzy control systems to take a practical, applications-based approach, backed up with worked examples and case studies*Learn to use VHDL in real-world applications

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.

In this book, two types of direct adaptive control schemes for a class of nonlinear systems are proposed. Based on the feedback linearization theory, the architecture employs for the first approach the fuzzy logic reasoning of Takagi Sugeno (TS) type and uses for the second approach the strategy of neural network reasoning of radial basis function (RBF) type to approximate the feedback linearization control law. In each case, the parameters of the adaptive controller are adapted according to a law derived using Lyapunov stability theory. The adaptive controller is applied in simulation to control three nonlinear systems in both the fuzzy and the neural network methods.

The combination of Artificial Neural Network and Fuzzy Logic are probably the most attractive techniques among the researchers in recent times which is capable of handling non-linear, imprecise, fuzzy, noisy and probabilistic information to solve complex problem in efficient manner. Hybrid systems are designed to take advantage of the strengths of each system and avoid the limitations of each system. It is natural for neural networks to learn but it is cumbersome for a fuzzy system to learn. Hence a combination of the two would result in a rule- based system that can learn and adapt. This book, therefore, provide a comprehensive and integrated approach using Fuzzy logic and Artificial neural network techniques in modeling selected hydrological problems related to International river Brahmaputra within India. Four different hydrological problems are modeled using the proposed fuzzy – neural network approach for examining the usefulness of it. This comprehensive real time hydrological modelling study should be especially useful to the Hydrologist, civil engineers, agriculturists, students, field engineers and related governmental as well as non-governmental organisations.

The emergence of computational intelligence technology inspired by biological and human intelligence is one of the most exciting and important fields in engineering . It is expected that these technologies like fuzzy logic , will play a significant role in the development of intelligent robotic systems , machine systems , and mechatronics systems . In this work ; a Fuzzy Logic Controller is presented to control the motion of a robotic arm and avoid the obstacles existed in its mission road ,using a real platform robotic arm in combination with a vision system . This work involves constructing an integrated and autonomic MATLAB program. It could be applicable for any robotic arm . It depends on a new approach in analyzing the robotic environment videos acquired by a fixed webcam. The approach uses colors to detect and recognize the changeable locations and objects’ dimensions for each of the robot’s end-effector , the goal , and the obstacles .

Modeling is a helpful tool that might be used to predict the Dissolved Oxygen (DO) level of a lake. Most ecological systems are complex and unstable. In case black box models might be essential instead of deterministic ones. DO in Eymir Lake was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate,Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: developing models with ANN to predict DO level in Lake Eymir with high fidelity to actual DO data, to compare the success of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. “Matlab R 2007b” software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.

Fuzzy Logic Control (FLC) is an important alternative method to the conventional Proportional, Integral, and Derivative (PID) control method for use in nonlinear systems. This book, therefore, highlight the feasibility and effectiveness of fuzzy logic control in application to mathematical models of two basic types of steam turbines; straight expansion and single-automatic extraction turbines. The derived performance of the developed mathematical models, in terms of input/output duty variables without mean of control, is found to be in a good agreement with the actual performance of typical steam turbines with practical technical data and operating conditions. Model components exhibit nonlinear behavior. A comparison is made between the efficiency of Fuzzy Logic Control and the conventional PID control for the dynamic responses of the closed loop drive system. In case of straight expansion steam turbines, the control task is either speed or backpressure control. In case of single extraction steam turbines, the control task is to maintain both speed and extraction pressure of the turbine constants. This is done in presence of severe changes in load and/or steam demand conditions.

Over the past few decades there has been massive increase in wireless applications usage and subsequently in allocation of available spectrum. The available spectrum is getting scarce because of this surge in wireless link usage. The scarcity is not only the physical shortage of spectrum but there is an inefficient and inflexible spectrum usage. In order to address the problem of underutilization of spectrum the cognitive radio network (CRN) technology has implemented. According to CRN technology radio users could have the cognitive capacity and adaptability according to different transmission environments what makes them able to transmit through the spectrum holes dynamically and opportunistically. Since in CRN the effective decision making plays a major role to maintain the QoS towards the users, we introduced the effective decision making systems here. In this book we combine Fuzzy logic Mathematical modeling tools with CRN for efficient spectrum usage. To challenge our analysis with real time conditions, we validate the system in multiple propagation environments and channel fading conditions. We implement OFDM technology in CRN to improve the data rate in emergency situation

This book considers the problem of oscillations in synchronous generator connected to infinite bus through transmission lines. Two control techniques namely; artificial neural networks and fuzzy logic will be used to cancel the oscillations in the synchronous generators. Simulation results of applying external disturbances to the synchronous generator controlled by the proposed fuzzy logic controllers are compared to results obtained by using neural network controllers. These controllers based on excitation control are used to improve the performance of the synchronous generator. The simulation results show the advantages and the improvements with using the proposed fuzzy logic controllers over using the proposed neural network controllers. The potentials of the proposed techniques are investigated in time and frequency domains by using MATLAB software package.

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.

Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day – operating forecasting; one-day-week-month-ahead load forecasting – short-term load forecasting; one-month-quarter-year-ahead load forecasting – long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.

A number of academic and industrial researches incontrol systems have exposed the inherent weaknessesof PID control which are; rigidity, prohibitivecomputational complexity and non-applicability forintelligent and complex systems. Consequently, agroup of researchers have proposed fuzzy logiccontrol as a better alternative to PID control. Thisnotion has spawned numerous debates amongresearchers, experts and professionals in the fieldof control systems. As a result, this bookinvestigates and compares the performance oftraditional control techniques with fuzzy logiccontrol which will be optimized and made adaptive tothe variations of the sensor input. It will also beproven that fuzzy logic control is far more superiorin performance to the existing traditional controltechniques. These objectives were achieved throughthe use of MATLAB and SIMULINK to simulate, tweak andfine-tune the different cases for the response andtheir respective performance metrics. Interestinglyas expected, the results of the simulations showsthat fuzzy logic control, optimized or not, is betterthan the traditional control techniques, especially,PID control.

## neural and fuzzy logic control of drives and power systems в наличии / купить интернет-магазине

## Neural and Fuzzy Logic Control of Drives and Power Systems

The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic, mathematical modelling of electrical machines, power systems control and VHDL design. Unlike the academic monographs that have previously been published on each of these subjects, this book combines them and is based round case studies of systems analysis, control strategies, design, simulation and implementation. The result is a guide to applied control systems design that will appeal equally to students and professional design engineers. The book can also be used as a unique VHDL design aid, based on real-world power engineering applications.*Introduces cutting-edge control systems to a wide readership of engineers and students*The first book on neuro-fuzzy control systems to take a practical, applications-based approach, backed up with worked examples and case studies*Learn to use VHDL in real-world applications

## 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.

## Fuzzy and Neural Adaptive Control of a Class of Nonlinear Systems

In this book, two types of direct adaptive control schemes for a class of nonlinear systems are proposed. Based on the feedback linearization theory, the architecture employs for the first approach the fuzzy logic reasoning of Takagi Sugeno (TS) type and uses for the second approach the strategy of neural network reasoning of radial basis function (RBF) type to approximate the feedback linearization control law. In each case, the parameters of the adaptive controller are adapted according to a law derived using Lyapunov stability theory. The adaptive controller is applied in simulation to control three nonlinear systems in both the fuzzy and the neural network methods.

## Fuzzy logic and artificial neural network for hydrological modeling

The combination of Artificial Neural Network and Fuzzy Logic are probably the most attractive techniques among the researchers in recent times which is capable of handling non-linear, imprecise, fuzzy, noisy and probabilistic information to solve complex problem in efficient manner. Hybrid systems are designed to take advantage of the strengths of each system and avoid the limitations of each system. It is natural for neural networks to learn but it is cumbersome for a fuzzy system to learn. Hence a combination of the two would result in a rule- based system that can learn and adapt. This book, therefore, provide a comprehensive and integrated approach using Fuzzy logic and Artificial neural network techniques in modeling selected hydrological problems related to International river Brahmaputra within India. Four different hydrological problems are modeled using the proposed fuzzy – neural network approach for examining the usefulness of it. This comprehensive real time hydrological modelling study should be especially useful to the Hydrologist, civil engineers, agriculturists, students, field engineers and related governmental as well as non-governmental organisations.

## Fuzzy logic control of a robotic manipulator for obstacles avoidance

The emergence of computational intelligence technology inspired by biological and human intelligence is one of the most exciting and important fields in engineering . It is expected that these technologies like fuzzy logic , will play a significant role in the development of intelligent robotic systems , machine systems , and mechatronics systems . In this work ; a Fuzzy Logic Controller is presented to control the motion of a robotic arm and avoid the obstacles existed in its mission road ,using a real platform robotic arm in combination with a vision system . This work involves constructing an integrated and autonomic MATLAB program. It could be applicable for any robotic arm . It depends on a new approach in analyzing the robotic environment videos acquired by a fixed webcam. The approach uses colors to detect and recognize the changeable locations and objects’ dimensions for each of the robot’s end-effector , the goal , and the obstacles .

## Neural Network and Fuzzy Logic Implementation on Lake Ecosystems

Modeling is a helpful tool that might be used to predict the Dissolved Oxygen (DO) level of a lake. Most ecological systems are complex and unstable. In case black box models might be essential instead of deterministic ones. DO in Eymir Lake was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate,Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: developing models with ANN to predict DO level in Lake Eymir with high fidelity to actual DO data, to compare the success of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. “Matlab R 2007b” software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.

## Feasibility Of Fuzzy Logic Control For Steam Turbine Systems

Fuzzy Logic Control (FLC) is an important alternative method to the conventional Proportional, Integral, and Derivative (PID) control method for use in nonlinear systems. This book, therefore, highlight the feasibility and effectiveness of fuzzy logic control in application to mathematical models of two basic types of steam turbines; straight expansion and single-automatic extraction turbines. The derived performance of the developed mathematical models, in terms of input/output duty variables without mean of control, is found to be in a good agreement with the actual performance of typical steam turbines with practical technical data and operating conditions. Model components exhibit nonlinear behavior. A comparison is made between the efficiency of Fuzzy Logic Control and the conventional PID control for the dynamic responses of the closed loop drive system. In case of straight expansion steam turbines, the control task is either speed or backpressure control. In case of single extraction steam turbines, the control task is to maintain both speed and extraction pressure of the turbine constants. This is done in presence of severe changes in load and/or steam demand conditions.

## Neural Network and Fuzzy Logic Applications in C/C++

Neural Network and Fuzzy Logic Applications in C/C++

## Neural Network and Fuzzy Logic Applications in C/C++

Neural Network and Fuzzy Logic Applications in C/C++

## Neural Network and Fuzzy Logic Applications in C/C++

Neural Network and Fuzzy Logic Applications in C/C++

## Fuzzy Logic based Power Control Techniques in Cognitive Radio Networks

Over the past few decades there has been massive increase in wireless applications usage and subsequently in allocation of available spectrum. The available spectrum is getting scarce because of this surge in wireless link usage. The scarcity is not only the physical shortage of spectrum but there is an inefficient and inflexible spectrum usage. In order to address the problem of underutilization of spectrum the cognitive radio network (CRN) technology has implemented. According to CRN technology radio users could have the cognitive capacity and adaptability according to different transmission environments what makes them able to transmit through the spectrum holes dynamically and opportunistically. Since in CRN the effective decision making plays a major role to maintain the QoS towards the users, we introduced the effective decision making systems here. In this book we combine Fuzzy logic Mathematical modeling tools with CRN for efficient spectrum usage. To challenge our analysis with real time conditions, we validate the system in multiple propagation environments and channel fading conditions. We implement OFDM technology in CRN to improve the data rate in emergency situation

## Excitation Control of Synchronous Generators

This book considers the problem of oscillations in synchronous generator connected to infinite bus through transmission lines. Two control techniques namely; artificial neural networks and fuzzy logic will be used to cancel the oscillations in the synchronous generators. Simulation results of applying external disturbances to the synchronous generator controlled by the proposed fuzzy logic controllers are compared to results obtained by using neural network controllers. These controllers based on excitation control are used to improve the performance of the synchronous generator. The simulation results show the advantages and the improvements with using the proposed fuzzy logic controllers over using the proposed neural network controllers. The potentials of the proposed techniques are investigated in time and frequency domains by using MATLAB software package.

## 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.

## Electric load forecasting using an artificial neural networks

Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day – operating forecasting; one-day-week-month-ahead load forecasting – short-term load forecasting; one-month-quarter-year-ahead load forecasting – long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.

## Design of Hybrid Fuzzy Logic Controllers

A number of academic and industrial researches incontrol systems have exposed the inherent weaknessesof PID control which are; rigidity, prohibitivecomputational complexity and non-applicability forintelligent and complex systems. Consequently, agroup of researchers have proposed fuzzy logiccontrol as a better alternative to PID control. Thisnotion has spawned numerous debates amongresearchers, experts and professionals in the fieldof control systems. As a result, this bookinvestigates and compares the performance oftraditional control techniques with fuzzy logiccontrol which will be optimized and made adaptive tothe variations of the sensor input. It will also beproven that fuzzy logic control is far more superiorin performance to the existing traditional controltechniques. These objectives were achieved throughthe use of MATLAB and SIMULINK to simulate, tweak andfine-tune the different cases for the response andtheir respective performance metrics. Interestinglyas expected, the results of the simulations showsthat fuzzy logic control, optimized or not, is betterthan the traditional control techniques, especially,PID control.