Matlab Program Library for Modeling and Simulating Control Systems for Electric Drives Based on Fuzzy Logic

Fuzzy control of the speed of electric drives is an alternative in the field of the control system. Modeling and simulation of electric drive control systems based on fuzzy logic is an important step in design and development. This chapter provides a complete means of modeling and simulation of fuzzy control systems for DC motors, induction motors, and permanent magnet synchronous motors, made in the Matlab/Simulink program environment, useful for performing complex analyzes. The functioning of the programs is demonstrated by an example of characteristics obtained practically, with a functioning regime often encountered in practice

Fuzzy operátoros módszerek alkalmazása az intelligens járműinformatikai rendszerekben = Applications of Fuzzy Operators in Intelligent Road Vehicle Informations Systems

A kutatásaink során összetett nemlineáris dinamikus rendszerek leírásának újszerű megközelítésével új és eredeti modellezési eljárásokat dolgoztunk ki a fuzzy és a tenzorszorzat operátoros módszerek alkalmazásával az intelligens járműinformatikai rendszerek területén. Ezen eljárások egy része a lineáris paraméterváltozójú (linear parameter varying system description) nemlineáris rendszerek keretén belül új kanonikus reprezentációkat dolgozott ki és alkalmazott, melyek alkalmasak bonyolult tudományos és műszaki feladatok megoldására. Az elméleti alkalmazások eredményesebb gyakorlati megvalósítása céljából ezen eljárásokat kiegészítettük a hozzájuk elméletileg is közel álló intelligens módszerek és eljárások fejlesztésével. Az így együttesen alkalmazott megközelítési mód a nagytömegű hatékony adatfeldolgozás mellett lehetővé teszi a szakértői tudás eredményes figyelembevételét is. Kutatásinkban lényeges előrelépés történt fuzzy rendszerek közelítő érvelése terén. A "közelítő" vagy "fuzzy" érvelés azt a gyakorlatban nagyon fontos esetet jelenti, amelyben valamilyen folyamatok révén várhatóan pontatlan következtetést lehet levonni szintén pontatlan előfeltételek halmazából. A modellezési eljárások kidolgozása kiterjedt különböző, az alkalmazások szempontjából fontos képfeldolgozási, alak felismerési, módszerek megalkotására is. A konkrét alkalmazások elsősorban a jármű és utas biztonság minőségének javítására irányultak az újonnan kidolgozott modellek alkalmazásával. | During our research we have developed novel approaches to describe and to model nonlinear dynamic systems mainly in the field of intelligent vehicle information systems by applying the fuzzy and tensor product operator based models and methods. One part of the research in the framework of linear parameter varying (LPV) nonlinear systems was aimed to develop and apply new canonical forms, which are useful to solve complex technical problems. In order to be able to apply these theoretical results in the practice we extend them with the development of intelligent methods and procedures. Such an approach beside efficient data processing enables to take into account the expert knowledge, as well. In our researches we improved the approximate reasoning of fuzzy systems. The approximate or fuzzy reasoning means the case, when trough some processes imprecise consequences can be concluded from the set of imprecise preconditions. The elaboration of modeling procedures covers various important image processing, shape recognition methods, as well. Moreover we successfully applied the developed methods also in the field of dynamic modeling of mechanical systems, where due to nonlinear phenomena only black box like approximations can be performed. The concretely applications aimed to improve the vehicle and passenger safety with the help of the developed novel methods. They may play an important role also by the design, implementation of an integrated intelligent transportation system

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The Levenberg–Marquardt (L–M) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The L–M method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e Innovación TEC2008-04920Junta de Andalucía P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626

A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance

Neuro-Fuzzy Computing System with the Capacity of Implementation on Memristor-Crossbar and Optimization-Free Hardware Training

In this paper, first we present a new explanation for the relation between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. Then, based on these results, we propose a new neuro-fuzzy computing system which can effectively be implemented on the memristor-crossbar structure. One important feature of the proposed system is that its hardware can directly be trained using the Hebbian learning rule and without the need to any optimization. The system also has a very good capability to deal with huge number of input-out training data without facing problems like overtraining.Comment: 16 pages, 11 images, submitted to IEEE Trans. on Fuzzy system