1,721,018 research outputs found
Evolving intelligent systems : methodology and applications.
No full textThe newly established concept of evolving intelligent systems is a result of the synergy between conventional systems, neural networks and fuzzy systems as structures for information representation and the real time methods for machine learning. It targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans. Neural Networks present a convenient framework for synthesis and analysis of complex non-linear systems. Neuro-fuzzy systems combine the advantages of both areas and often use established machine learning methods for design. One of the important research challenges today is to further develop the intelligent systems theory towards the design of truly intelligent systems with a higher level of flexibility and autonomy that can develop their understanding of the environment and ultimately their intelligence. To address the problems of modelling, control, prediction, classification and data processing in such environments a system must be able to fully adapt its structure rather than adjust its parameters based on a pre-trained and fixed structure. That is, the system must be able to evolve, to self-develop, to self-organize. Evolving intelligent systems are inspired by the idea of system model evolution. They focus on the evolution of an open family of system models representing the system in different situations and operating conditions. In this sense they differ from the traditional evolutionary algorithms. They use inheritance and gradual change with the aim of life-long learning and adaptation, self-organization (including system structure evolution) in order to adapt to the (unknown and unpredictable) environment. Embedded soft computing diagnostics and prognostics algorithms, intelligent agents and controllers are the natural implementation area of evolving systems as a realistic and practical tool for design of real time intelligent systems
Evolving Fuzzy Systems, Proc. of the 2006 International Symposium on Evolving Fuzzy Systems EFS'06.
No full textA key characteristic of intelligent systems is their ability to deduce new knowledge, to predict and make decisions. The theoretical underpinning of these capabilities mainly resides in approximate reasoning, which itself is based on fuzzy logic and fuzzy sets. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans. The main challenge today is to design the next generation of intelligent systems able to have a higher level of flexibility and autonomy that can develop their understanding of the environment and ultimately their intelligence. It is to be noticed that the environments in which such systems are required to successfully operate are very often challenging – they are non-stationary, (often unpredictably) changing, and partially or completely unknown. To address the problems of modeling, control, prediction, classification and data processing in such environments a system model must be able to fully adapt, not simply to adjust parameters of a pre-trained and fixed structure. That is, the system model must be able to evolve, to self-develop, to self-organize. A new area that is addressing these challenges is now emerging on the cross-roads of reasoning-based fuzzy systems and evolution-inspired principles of adaptation, life-long learning, self-development and self-organization. It aims to develop systems that are more flexible than conventional adaptive systems that usually assume linearity and fixed structures of the underlying models. The emerging area of evolving fuzzy systems targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. Evolving fuzzy systems are evolution-inspired. They focus on the evolution of individual fuzzy systems. They use inheritance and gradual change with the aim of life-long learning and adaptation, as well as self-organization (including system structure evolution) in order to adapt to the (unknown and unpredictable) environment. This complements well with the established area of genetic fuzzy systems (GFS), that uses techniques based on population-based evolutionary and bio-inspired algorithms (genetic algorithms, genetic programming, evolution strategies, particle swarm optimization methods, bio-memetic approaches, ant colony optimization, etc.) to design fuzzy systems. These two topics make the methodological body of the present Symposium, which is the second in the series of events organized by the GFS Task Force from the Fuzzy Systems Technical Committee, IEEE Computational Intelligence Society. The present Symposium is organized by the Department of Communication Systems, Infolab21, Faculty of Science and Technology, Lancaster University, and technically co-sponsored by the Computational Intelligence Society and Systems, Man, and Cybernetics Society, IEEE, by the International Fuzzy Systems Association (IFSA), and by the European Society on Fuzzy Logic and Technology (EUSFLAT). The Symposium was also generously sponsored by EPSRC-UK arranging a number of student and young researchers’ grants that made possible attendance for a number of talented young people, and co-sponsored by Nokia-UK, BAE Systems, Retail Analytics, and J&S Marine offering a range of ‘best paper’ awards. This volume collects 55 full papers by 122 authors from 23 countries from all continents that have been accepted after being evaluated by at least three independent referees from the International Program Committee. All together, 72 papers authored by 153 authors from 26 countries were submitted and all of them were subjected to anonymous peer review process. The rejection rate of 24% (due to quality or irrelevance) is one of the illustrations of the high quality. The overall number of authors who have contributed to the symposium is another illustration of the International status of the event. The presence of several IEEE Fellows and a number of Senior Members and IEEE Technical Committees members is another fact in support of this qualification. Bearing in mind that this is only the second event of this series, the significant industrial participation (General Electric, USA, Ford Motor Co., USA, Nokia-UK, CEPSA, Spain etc.) illustrates the applicability of the methodologies that are discussed at the Symposium. The structure of the Symposium is composed of two main parts and eleven sessions: i) methodology-based sessions (covering evolving Takagi-Sugeno fuzzy models, evolving neuro-fuzzy systems, evolving fuzzy clustering, evolutionary fuzzy systems, genetic fuzzy systems); and ii) applications-oriented sessions (including industrial applications of real-time evolving fuzzy systems, evolving neuro-fuzzy systems, genetic fuzzy systems, developments in fuzzy systems and a session comprising papers that will discuss and explore the frontiers of computational intelligence and will pose some challenging questions. The discussion will take place in addition to scheduled sessions and networking events during the Round Table. (c) IEEE Pres
Aplicação de lógica fuzzy no controle de trânsito urbano
Full text linkDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.Este trabalho apresenta o desenvolvimento de uma aplicação de um sistema de controle de tráfego urbano baseado em um modelo utilizando lógica fuzzy. As mudanças ocorridas em nossa sociedade nos últimos anos com o crescimento populacional e o aumento do número de veículos em vias públicas trouxeram problemas como o tráfego intenso e congestionamentos de trânsito. Diariamente perde-se muito tempo em cruzamentos e paradas, e há freqüentes acidentes em vias, trazendo não só prejuízos econômicos, mas também transtornos para o bem estar e para a qualidade de vida das pessoas. Nesta dissertação propõe-se uma contribuição visando amenizar esses inconvenientes. Para tal, a mesma vale-se da ciência da computação e de suas técnicas de inteligência artificial, introduzindo um sistema inteligente de controle de tráfego que melhore o fluxo de atendimento de veículos que concorrem em uma dada interseção, controlando o tempo de ciclo dos semáforos e baseando-se em sistemas atuais existentes de engenharia de tráfego
Evolving Takagi-Sugeno fuzzy systems from data streams (eTS+).
No full textIt is a well known fact that nowadays we are faced with not only large data sets that we need to process quickly, but with huge data streams (Domingos and Hulten, 2001). Special requirements are also placed by the fast growing sector of autonomous systems where systems that can re-train and adapt ‘on-fly’ are required (Patchett and Sastri, 2007). Similar requirements are enforced by the advanced process industries for self-developing and self-maintaining sensors (Qin et al., 1997). Now they even talk about self-learning industries (EC, 2007). All of these requirements cannot be met by using off-line methods and systems that can only adjust their parameters and/or are linear (Astroem and Wittenmark, 1989). These requirements call for a new type of systems that assumes the structure of non-linear, non-stationary systems to be adaptive and flexible. The author of this chapter started research work in this direction around the turn of the century (Angelov and Buswell, 2001; Angelov, 2002) and this research culminated in proposing with Dr. D. Filev the so called evolving Takagi-Sugeno (eTS) fuzzy system (Angelov and Filev, 2003). Since then a number of improvements of the original algorithm has been done, which require a systematic description in one publication. In this chapter an enhanced version of the eTS algorithm will be described which is called eTS+. It has been tested on a data stream from real engine test bench (data provided courtesy of Dr. E. Lughofer, Linz, Austria). The results demonstrate the superiority of the proposed enhanced approach for modeling real data stream in precision, simplicity and interpretability, and computational resources used. (c) IEEE Press and John Wiley and Son
Evolving intelligent sensors in chemical industry.
No full textThis chapter presents a new promising technique for design of inferential sensors in chemical process industry which has a broad range of applicability. It is based on the concept of evolving fuzzy rule-based systems (EFS). Mathematical modeling was used to infer difficult to measure otherwise variables such as product quality since 1980s, including in on-line mode. The challenge today is to develop such adaptive, flexible, self-calibrating on-line inferential sensors that reduce the maintenance costs while keeping high precision and interpretability/transparency. The methodology of fuzzy rule-based models of Takagi-Sugeno type (Takagi-Sugeno, 1985) which have flexible, open structure and are therefore called ‘evolving’ (see also chapter 2) is particularly suitable for addressing this challenge. The basic concept is described from the point of view of the implementation of this technique to self-maintaining and self-calibrating inferential sensors for several chemical industry processes. The sensitivity analysis (input variables selection) was performed on-line and this was compared to the off-line input variables selection using genetic programming (GP). A case study based on four different inferential sensors for estimating chemical properties is presented in more detail, while the methodology and conclusions are valid for the broader area of chemical and process industry in general. The results demonstrate that well interpretable and with simple structure inferential sensors can be designed automatically from the data stream in real-time that provide estimation of the real values of process variables of interest. The proposed approach can be used as a basis for development of a new generation of inferential sensors that can address the challenges of the modern advanced process industry. (c) IEEE Press and John Wiley and Son
Applications of evolving intelligent systems to oil and gas industry.
No full textThe Oil and Gas (O&G) industry has a variety of characteristics that make it suitable for application of Evolving Intelligent Systems (EIS). One can summarize these in: i. A need for constant product monitoring; ii. Low margin and high throughput; iii. A regulated market for the products and a free market for the crude; iv. High process complexity; v. Expensive process investment; vi. High number of process variables; vii. High number of process products; The raw material for refineries is crude oil. There are a vast variety of crudes with different properties that will give different cuts and yields when processing. The price of crude depends on several factors, ranging from its intrinsic chemical properties to political stability in the country of supply and short and long-term market behavior. Crude oil purchase department has to balance spot market and long term contracts to maintain a stable crude supply to their refineries. Yields in crude have to fit refineries complexity to balance the local consumption. Any violation of the balance is normally undesirable. Oil refining activities are characterized by a low margin, high throughput. International energy market is free but local market is normally highly regulated. Therefore, lowering the production cost is the best option for refineries to survive and they have to produce as much intermediate materials as possible. Importing intermediate components is not optimal. For a process industry with such characteristics, the on-line process monitoring and control is very important. The processes in an oil refinery generate huge volumes and streams of data that are routinely stored in huge databases. The operator of a typical Distributed Control System (DCS) in a complex contemporary oil refinery controls and monitors several process units and has the responsibility for more than 400 valves! A typical oil refinery database can contain as much as 10,000-12,000 continuous data points. Laboratory samples are routinely analyzed and more than 2000 characteristics are reported every day. The process operators continuously (in ‘real-time’) make decisions based on previous experience to drive the process towards targets. With evolving intelligent sensors this process can be automated. An oil refinery usually produces a very high number of products, which ranges from light hydrocarbons to heavy fuels. There are a high number of legal specifications that impacts the process economics. It is normal practice to blend several intermediate products and recipes in various combinations in order to produce different final products. All these combinations have to meet legal specifications. The balance between specifications and product components give the degrees of freedom and emphasizes the need for on-line monitoring of the quality of the products. (c) IEEE Press and John Wiley and Son
Data fusion via fission for the analysis of brain death
No full textInformation fusion via signal fission is addressed in the framework of empirical mode decomposition (EMD) to determine brain death in deep coma patients. In this way, a general nonlinear and nonstationary brain signal is decomposed into its oscillatory components (fission); the components of interest are then combined in an ad-hoc or automated fashion in order to provide greater knowledge about a process in hand (fusion). This chapter illustrates howthe fusion via fission methodology can be used to retain components of interest in electroencephalography (EEG), thus highlighting the absence or presence of brain death. Additionally, it is shown howcomplex extensions of the algorithm can be used to detect phase synchronization by simulations and applications to EEG signals
TEAM: A Parameter-Free Algorithm to Teach Collaborative Robots Motions from User Demonstrations
No full textLearning from demonstrations (LfD) enables humans to easily teach collaborative robots (cobots) new motions that can be generalized to new task configurations without retraining. However, state-of-the-art LfD methods require manually tuning intrinsic parameters and have rarely been used in industrial contexts without experts. We propose a parameter-free LfD method based on probabilistic movement primitives, where parameters are determined using Jensen-Shannon divergence and Bayesian optimization, and users do not have to perform manual parameter tuning. The cobot’s precision in reproducing learned motions, and its ease of teaching and use by non-expert users are evaluated in two field tests. In the first field test, the cobot works on elevator door maintenance. In the second test, three factory workers teach the cobot tasks useful for their daily workflow. Errors between the cobot and target joint angles are insignificant—at worst 0.28 deg—and the motion is accurately reproduced—GMCC score of 1. Questionnaires completed by the workers highlighted the method’s ease of use and the accuracy of the reproduced motion. Public implementation of our method and datasets are made available online.BIORO
- …
