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.
The dissertation is devoted to the decision of the problems directed to the development of methods, models and algorithms for solving forecasting problems of financial risks under conditions of uncertainty, and for a complex of the problems related with it. These are fuzzy mathematics operations, the solving of linear algebraic equations system with fuzzy numbers (variables) in the neural network logic basis. This allows essentially raising the level of support of decision-making in the conditions of uncertainty and, as consequence from this, control efficiency. As a result of this, the mechanism of fuzzy conclusion in neural network logic basis is studied, namely it was suggested to use a connectional neural network, realizing the technique of fuzzy conclusion particularly, and fuzzy modeling in general. The problem of optimal borrower selection is realized in the program shell of the MATLAB/Fuzzy Sets Toolbox on current data.
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.
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 Book Modeling Quality of Service (QoS) in a Telecommunications Network using Neuro-fuzzy logic, this is generally the analysis of GSM theory as a telecommunications network and ANFIS as a non-linear modeling tool. The ANFIS-based model developed has demonstrated that it can be used to model Quality of Service using Logical Control and Traffic Channels key performance indicators, due to its degree of consistency. The statistical analysis has further ascertained and confirmed the accuracy of the ANFIS-based model developed.
The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. MR images also always contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques, for instance, neural networks, fuzzy logic, neuro fuzzy have shown great potential in this field. Hence, in this project the neuro fuzzy system or ANFIS was applied for classification and detection purposes. Decision making was performed in two stages: feature extraction using the principal component analysis (PCA) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the tumors.
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration,
Petri nets are considered to be the alternative approach for analysis, design and implementation of DES. However, the prospect of uncertain and vague information still creates a problem. Hence, to handle the uncertainty, fuzzy logic is integrated into Petri nets to form Fuzzy Automation Petri nets as a modeling tool to handle these uncertainties. The use of Fuzzy Automation Petri nets (FAPN) demands a valid translation of the formalism to Ladder Logic Diagrams for implementation. This is achieved using Token Passing Methodology. This work investigates the use of Fuzzy Petri nets in supervisory control and suggests a modified and improved version called Fuzzy Automation Petri net (FAPN) as a modeling tool. It presents a systematic approach to the synthesis of Fuzzy Petri net based supervisor for the forbidden state problem using supervisory design procedure. The controlled model of the system can be constructed from this FAPN net structure. The implementation is using Flexible Manufacturing System as an example of DES.
This book is all about the application of fuzzy logic technique for the energy planning process. It demonstrates the use of fuzzy goal programming model for energy resource allocation at the micro level, where the energy resources and needs are uncertain. This book presents an integrated planning approach by adopting multinomial logit model to household fuel choice behaviour for heating and cooking end-uses; and applies real option model for the policy evaluation of biogas and solar water heater system in the study region. This book is very useful for researchers who wish to work in the area of integrated fuzzy logic and energy resource planning.
Information retrieval system is heart of information system. The primary purpose of establishing an information retrieval system lies in assisting the users to effectively acquire drsired information. That is, users' query must be properly understood and answered. The present work falls in the area of information retrieval and to be more specific : query processing of information retrieval. This has been influenced by the limitation and disadvantages of the commercially available Boolean logic retrieval model. The limitation and disadvantages of the query processing of the Boolean logic model have been pointed out and logical solution using fuzzy set theory and fuzzy logic have been presenteted.
Well designed signs and symbols provide supplemental information to properly guide motorists to attractions and improve nearby traffic flow and safety. Major Traffic Generators (MTGs) are important regional attractions, events, or facilities, which attract individuals or groups from beyond a local community, city, or metropolitan areas, and require even more proper guide signing due to the large amount of attendance. Concise and legible symbols and/or signs, if properly designed and placed, should be helpful in alleviating traffic impacts by MTGs to surrounding roadway networks. Therefore it is necessary to establish the eligible criteria and warrants for MTG guide signs. Possible MTG criteria include community population, site generated traffic, parking space, proximity to major corridors, etc., which are normally based on engineers’ experiences and opinions, and thus are hard to be quantified and formulated. In this book, the fuzzy logic based methodology is proposed to synthesize the eligible MTG criteria for MTGs through algorithms based on the summary of existing manuals in some states in U.S., and on expert knowledge from a survey to engineers in traffic operations.
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.
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