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1
Sisman, Yilmaz Nuran Arzu. "A Temporal Neuro-fuzzy Approach For Time Series Analysis." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/570366/index.pdf.
Full textAbstract:
The subject of this thesis is to develop a temporal neuro-fuzzy system for fore-
casting the future behavior of a multivariate time series data.
The system has two components combined by means of a system interface.
First, a rule extraction method is designed which is named Fuzzy MAR (Multivari-
ate Auto-regression). The method produces the temporal relationships between
each of the variables and past values of all variables in the multivariate time series
system in the form of fuzzy rules. These rules may constitute the rule-base in a
fuzzy expert system.
Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in -
time is designed in order to make the use of fuzzy rules, to provide an environment
that keeps temporal relationships between the variables and to forecast the future
behavior of data. The rule base of ANFIS unfolded in time contains temporal
TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation
learning algorithm is used. The system takes the multivariate data and the num-
ber of lags needed which are the output of Fuzzy MAR in order to describe a
variable and predicts the future behavior.
Computer simulations are performed by using synthetic and real multivariate
data and a benchmark problem (Gas Furnace Data) used in comparing neuro-
fuzzy systems. The tests are performed in order to show how the system efficiently
model and forecast the multivariate temporal data. Experimental results show
that the proposed model achieves online learning and prediction on temporal data.
The results are compared by other neuro-fuzzy systems, specifically ANFIS.
2
Osut, Demet. "A Behavior Based Robot Control System Using Neuro-fuzzy Approach." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/109765/index.pdf.
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In autonomous navigation of mobile robots the dynamic environment is a source of problems. Because it is not possible to model all the possible conditions, the key
point in the robot control is to design a system that is adaptable to different conditions and robust in dynamic environments.
This study presents a reactive control system for a Khepera robot with the ability to navigate in a dynamic environment for reaching goal objects. The main motivation of
this research is to design a robot control, which is robust to sensor errors and sudden changes and adaptable to different environments and conditions. Behavior based
approach is used with taking the advantage of fuzzy reasoning in design.
Experiments are made on Webots, which is a simulation environment for Khepera robot.
3
Arslan, Dilek. "A Control System Using Behavior Hierarchies And Neuro-fuzzy Approach." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605743/index.pdf.
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In agent based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainity and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle these uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task using an environment, which has randomly placed obstacles and a goal position to simulate an environment similar to an autonomous robot&rsquos indoor environment. Then the control system was extended to control an agent in a multi-agent environment. The main motivation of this study is to design a control system which is robust to errors and easy to modify. Behaviour based approach with the advantages of fuzzy reasoning systems is used in the system.
4
OLIVEIRA, CARLOS ALEXANDRE DOS SANTOS. "STRATEGIC GROUPS: ARESOURCE-BASED VIEW AND NEURO-FUZZY SYSTEMS APPROACH." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5856@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRODesde sua formulação, no início da década de setenta, o conceito de grupo estratégico é objeto de pesquisas teóricas e empíricas que buscam confirmar sua existência, sua contribuição à avaliação da performance e à formação das estratégias das empresas. Este trabalho soma-se a estas pesquisas, utilizando os conceitos da Visão Resource- Based e a aplicação de ferramentas de inteligência computacional, neste caso as redes neurais e os sistemas de inferência fuzzy, com o objetivo de contribuir para a discussão deste tema na superação de suas limitações e dos novos desafios que o aumento da complexidade das arenas competitivas trouxeram para as pesquisas do gerenciamento estratégico. A Visão Resource-Based fornece a base teórica para o desenvolvimento dos construtos: grau de inimitabilidade e grau de imobilidade, resultantes da exploração estratégica dos recursos da empresa. Estes construtos são propostos como dimensões de avaliação da semelhança estratégica entre as empresas de uma arena competitiva. A inteligência computacional fornece os meios de extração de informações subjetivas, e presentes em ambientes complexos, através da simulação do aprendizado, percepção, evolução e adaptação do raciocínio humano. O resultado é a proposição de um modelo de avaliação da existência de grupos estratégicos, utilizando os construtos Grau de Inimitabilidade e Grau de Imobilidade, e Sistemas Neuro-fuzzy. Este modelo é aplicado ao setor de supermercados como teste de validação do mesmo.
Since its has introduced, in the beginning of the decade of seventy, the concept of strategic groups is object of theoretical and empirical research that aims to confirm its existence, its contribution to performance evaluation and the formulation of the strategies of the firms. This text join these research, using the Resource-Based Views framework and soft computing, in this case neural networks and fuzzy inference systems, with aims at contributing for the discussion of this subject to overcome its limitations and the new challenges, resulting increasingly complexity and competitive environment, for the strategic management research. The Resource-Based View framework supplies the theoretical underpinnings to use the inimitability degree and immobility degree, resultants of the strategical exploration of the resources of the firms, as constructors to evaluate firm strategic similarity in a competitive environment. Soft computing is a tool to extract subjective data from complexity environments, simulating the ability for learning, perception, evolution and adaptation of human reasoning. The result of this research is the proposal of a model to identify strategic groups, applying the constructors Inimitability Degree and Immobility Degree, and Neuro-fuzzy Inference Systems. To validate the model, a test is performed to the supermarkets industry.
5
Wang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.
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It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to calculate the accurate magnitude of feedback control actions in traditional SPC. Suitable feedback adjustments are generated from the experiences of process engineers. This drawback means that the SPC technique can not be directly applied in an automatic system. This thesis is concerned with Fuzzy Sets and Fuzzy Logic applied to the uncertainty of relationships between the SPC (early stage) alarms and SPC implementation. Based on a number of experiments of the frequency distribution for shifts of abnormal process averages and human subjective decision, a Fuzzy-SPC control system is developed to generate the magnitude of feedback control actions using fuzzy inference. A simulation study which is written in C++ is designed to implement a Fuzzy-SPC controller with satisfactory results. To further reduce the control errors, a NeuroFuzzy network is employed to build NNFuzzy- SPC system in MATLAB. The advantage of the leaning capability of Neural Networks is used to optimise the parameters of the Fuzzy- X and Fuzzy-J? controllers in order to obtain the ideal consequent membership functions to adapt to the randomness of various processes. Simulation results show that the NN-Fuzzy-SPC control system has high control accuracy and stable repeatability. To further improve the practicability of a NN-Fuzzy-SPC system, a combined forecaster with EWMA chart and digital filter is designed to reduce the NN-Fuzzy-SPC control delay. For the EWMA chart, the smoothing constant 0 is investigated by a number of experiments and optimised in the forecast process. The Finite Impulse Response (FIR) lowpass filter is designed to smooth the input data (signal) fluctuations in order to reduce the forecast errors. An improved NN-Fuzzy-SPC control system which shows high control accuracy and short control delay can be applied in both automatic control and online quality control.
6
Kim, Sungshin. "A neuro-fuzzy approach to optimization and control of complex nonlinear processes." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14820.
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Mountrakis, Georgios. "Context-Specific Preference Learning of One Dimensional Quantitative Geospatial Attributes Using a Neuro-Fuzzy Approach." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/MountrakisGX2004.pdf.
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Taghizadeh, Vahed Amir. "Fan And Pitch Angle Selection For Efficient Mine Ventilation Using Analytical Hierachy Process And Neuro Fuzzy Approach." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614320/index.pdf.
Full textAbstract:
Ventilation is a critical task in underground mining operation. Lack of a good ventilation system causes accumulation of harmful gases, explosions, and even fatalities. A proper ventilation system provides adequate fresh air to miners for a safe and comfortable working environment. Fans, which provide air flow to different faces of a mine, have great impact in ventilation systems. Thus, selection of appropriate fans for a mine is the acute task. Unsuitable selection of a fan decreases safety and production rate, which increases capital and operational costs. Moreover, pitch angle of fans&rsquoblades plays an important role in fan&rsquo
s efficiency. Therefore, selection of a fan and its pitch angle, which yields the maximum efficiency, is an emerging issue for an efficient mine ventilation. The main objective of this research study is to provide a decision making methodology for the selection of a main fan and its appropriate pitch angle for efficient mine ventilation. Nowadays, analytical hierarchy process as multi criteria decision making is used, and it yields outputs based on pairwise comparison. On the other hand, Fuzzy Logic as a soft computing method was combined with analytical hierarchy process and combined model did not yield appropriate results
because Fuzzy AHP increased uncertainty ratio in this study. However, fuzzy analytical hierarchy process might be inapplicable when it faces with vague and complex data set. Soft computing methods can be utilized for complicated situations. One of the soft computing methods is a Neuro-Fuzzy algorithm which is used in classification and DM issues. This study has two phases: i) selection of an appropriate fan using Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and ii) selection of an appropriate pitch angle using Neuro-Fuzzy algorithm and Fuzzy AHP method. This study showed that AHP can be effectively utilized for main fan selection. It performs better than Fuzzy AHP because FAHP contains more expertise and makes problems more complex for evaluating. When FAHP and Neuro-Fuzzy is compared for pitch angle selection, both methodologies yielded the same results. Therefore, utilization of Neuro-Fuzzy in situation with complicated and vague data will be applicable.
9
[Verfasser], Habtamu Gezahegn Tolossa, and Silke [Akademischer Betreuer] Wieprecht. "Sediment transport computation using a data-driven adaptive neuro-fuzzy modelling approach / Habtamu Gezahegn Tolossa. Betreuer: Silke Wieprecht." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/1024692574/34.
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KOTHAMASU, RANGANATH. "INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141339344.
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Jneid, Khoder. "Apprentissage par Renforcement Profond pour l'Optimisation du Contrôle et de la Gestion des Bâtiment." Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALM062.
Full textAbstract:
Les systèmes de chauffage, de ventilation et de climatisation (CVC) consomment une quantité important d'énergie dans les bâtiments. Les approches conventionnelles utilisées pour contrôler les systèmes CVC reposent sur un contrôle basé sur des règles (RBC) qui consiste en des règles prédéfinies établies par un expert. Le contrôle prédictif par modèle (MPC), largement exploré dans la littérature, n'est pas adopté par l'industrie car il s'agit d'une approche basée sur un modèle qui nécessite de construire au préalable des modèles du bâtiment qui sont utilisés dans la phase d'optimisation. Cette construction initiale de modèle est coûteuse et il est difficile de maintenir ces modèles au cours de la vie du bâtiment. Au cours de la thèse, nous étudions l'apprentissage par renforcement (RL) pour optimiser la consommation d'énergie des systèmes CVC tout en maintenant un bon confort thermique et une bonne qualité de l'air. Plus précisément, nous nous concentrons sur les algorithmes d'apprentissage par renforcement sans modèle qui apprennent en interagissant avec l'environnement (le bâtiment, y compris le système CVC) et qui ne nécessitent donc pas de modèles précis de celui-ci. En outre, les approches en ligne sont prises en compte. Le principal défi d'un RL sans modèle en ligne est le nombre de jours nécessaires à l'algorithme pour acquérir suffisamment de données et de retours d'actions pour commencer à agir correctement. L'objectif de cette thèse est d'accélérer l'apprentissage les algorithmes RL sans modèle pour converger plus rapidement afin de les rendre applicables dans les applications du monde réel, le contrôle du chauffage, de la ventilation et de la climatisation. Deux approches ont été explorées au cours de la thèse pour atteindre notre objectif : la première approche combine la RBC avec la RL basé sur la valeur, et la seconde approche combine les règles floues avec le RL basé sur la politique. La première approche exploite les règles RBC pendant l'apprentissage, tandis que dans la seconde, les règles floues sont injectées directement dans la politique. Les tests sont effectués sur un bureau simulé, réplique d'un bureau réeel dans le bâtiment de Grenoble INP pendant la période hivernaleHeating, ventilation, and air-conditioning (HVAC) systems account for high energy consumption in buildings. Conventional approaches used to control HVAC systems rely on rule-based control (RBC) that consists of predefined rules set by an expert. Model-predictive control (MPC), widely explored in literature, is not adopted in the industry since it is a model-based approach that requires to build models of the building at the first stage to be used in the optimization phase and thus is time-consuming and expensive. During the PhD, we investigate reinforcement learning (RL) to optimize the energy consumption of HVAC systems while maintaining good thermal comfort and good air quality. Specifically, we focus on model-free RL algorithms that learn through interaction with the environment (building including the HVAC) and thus not requiring to have accurate models of the environment. In addition, online approaches are considered. The main challenge of an online model-free RL is the number of days that are necessary for the algorithm to acquire enough data and actions feedback to start acting properly. Hence, the research subject of the PhD is boosting model-free RL algorithms to converge faster to make them applicable in real-world applications, HVAC control. Two approaches have been explored during the PhD to achieve our objective: the first approach combines RBC with value-based RL, and the second approach combines fuzzy rules with policy-based RL. Both approaches aim to boost the convergence of RL by guiding the RL policy but they are completely different. The first approach exploits RBC rules during training while in the second approach, the fuzzy rules are injected directly into the policy. Tests areperformed on a simulated office during winter. This simulated office is a replica of a real office at Grenoble INP
12
Palancioglu, Haci Mustafa. "Extracting Movement Patterns Using Fuzzy and Neuro-fuzzy Approaches." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/PalanciogluHM2003.pdf.
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Silva, Sanchez Rosa Elvira. "Contribution au pronostic de durée de vie des systèmes piles à combustible PEMFC." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2005/document.
Full textAbstract:
Les travaux de cette thèse visent à apporter des éléments de solutions au problème de la durée de vie des systèmes pile à combustible (FCS – Fuel Cell System) de type à « membrane échangeuse de protons » (PEM – Proton Exchange Membrane) et se décline sur deux champs disciplinaires complémentaires :Une première approche vise à augmenter la durée de vie de celle-ci par la conception et la mise en œuvre d'une architecture de pronostic et de gestion de l'état de santé (PHM – Prognostics & Health Management). Les PEM-FCS, de par leur technologie, sont par essence des systèmes multi-physiques (électriques, fluidiques, électrochimiques, thermiques, mécaniques, etc.) et multi-échelles (de temps et d'espace) dont les comportements sont difficilement appréhendables. La nature non linéaire des phénomènes, le caractère réversible ou non des dégradations, et les interactions entre composants rendent effectivement difficile une étape de modélisation des défaillances. De plus, le manque d'homogénéité (actuel) dans le processus de fabrication rend difficile la caractérisation statistique de leur comportement. Le déploiement d'une solution PHM permettrait en effet d'anticiper et d'éviter les défaillances, d'évaluer l'état de santé, d'estimer le temps de vie résiduel du système, et finalement, d'envisager des actions de maîtrise (contrôle et/ou maintenance) pour assurer la continuité de fonctionnement. Une deuxième approche propose d'avoir recours à une hybridation passive de la PEMFC avec des super-condensateurs (UC – Ultra Capacitor) de façon à faire fonctionner la pile au plus proche de ses conditions opératoires optimales et ainsi, à minimiser l'impact du vieillissement. Les UCs apparaissent comme une source complémentaire à la PEMFC en raison de leur forte densité de puissance, de leur capacité de charge/décharge rapide, de leur réversibilité et de leur grande durée de vie. Si l'on prend l'exemple des véhicules à pile à combustible, l'association entre une PEMFC et des UCs peut être réalisée en utilisant un système hybride de type actif ou passif. Le comportement global du système dépend à la fois du choix de l'architecture et du positionnement de ces éléments en lien avec la charge électrique. Aujourd'hui, les recherches dans ce domaine se focalisent essentiellement sur la gestion d'énergie entre les sources et stockeurs embarqués ; et sur la définition et l'optimisation d'une interface électronique de puissance destinée à conditionner le flux d'énergie entre eux. Cependant, la présence de convertisseurs statiques augmente les sources de défaillances et pannes (défaillance des interrupteurs du convertisseur statique lui-même, impact des oscillations de courant haute fréquence sur le vieillissement de la pile), et augmente également les pertes énergétiques du système complet (même si le rendement du convertisseur statique est élevé, il dégrade néanmoins le bilan global)This thesis work aims to provide solutions for the limited lifetime of Proton Exchange Membrane Fuel Cell Systems (PEM-FCS) based on two complementary disciplines:A first approach consists in increasing the lifetime of the PEM-FCS by designing and implementing a Prognostics & Health Management (PHM) architecture. The PEM-FCS are essentially multi-physical systems (electrical, fluid, electrochemical, thermal, mechanical, etc.) and multi-scale (time and space), thus its behaviors are hardly understandable. The nonlinear nature of phenomena, the reversibility or not of degradations and the interactions between components makes it quite difficult to have a failure modeling stage. Moreover, the lack of homogeneity (actual) in the manufacturing process makes it difficult for statistical characterization of their behavior. The deployment of a PHM solution would indeed anticipate and avoid failures, assess the state of health, estimate the Remaining Useful Lifetime (RUL) of the system and finally consider control actions (control and/or maintenance) to ensure operation continuity.A second approach proposes to use a passive hybridization of the PEMFC with Ultra Capacitors (UC) to operate the fuel cell closer to its optimum operating conditions and thereby minimize the impact of aging. The UC appear as an additional source to the PEMFC due to their high power density, their capacity to charge/discharge rapidly, their reversibility and their long life. If we take the example of fuel cell hybrid electrical vehicles, the association between a PEMFC and UC can be performed using a hybrid of active or passive type system. The overall behavior of the system depends on both, the choice of the architecture and the positioning of these elements in connection with the electric charge. Today, research in this area focuses mainly on energy management between the sources and embedded storage and the definition and optimization of a power electronic interface designated to adjust the flow of energy between them. However, the presence of power converters increases the source of faults and failures (failure of the switches of the power converter and the impact of high frequency current oscillations on the aging of the PEMFC), and also increases the energy losses of the entire system (even if the performance of the power converter is high, it nevertheless degrades the overall system)
14
Alshejari, Abeer. "Neuro-fuzzy based intelligent approaches to nonlinear system identification and forecasting." Thesis, University of Westminster, 2018. https://westminsterresearch.westminster.ac.uk/item/q5w11/neuro-fuzzy-based-intelligent-approaches-to-nonlinear-system-identification-and-forecasting.
Full textAbstract:
Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems.
15
Uppal, Faisel J. "A critical study of neuro-fuzzy and decoupling approaches to monitoring of dynamic systems." Thesis, University of Hull, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397070.
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Winter, Maximilian [Verfasser], Christian W. M. [Akademischer Betreuer] Breitsamter, Christian W. M. [Gutachter] Breitsamter, and Stefan [Gutachter] Görtz. "Nonlinear Aerodynamic Reduced-Order Modeling Using Neuro-Fuzzy Approaches / Maximilian Winter ; Gutachter: Christian W. M. Breitsamter, Stefan Görtz ; Betreuer: Christian W. M. Breitsamter." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1230985239/34.
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Lin, Wen-Sheng, and 林文勝. "A Neuro-Fuzzy Approach for Classificaion." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/50709106002275282041.
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碩士國立中山大學
電機工程學系研究所
92
We develop a neuro-fuzzy network technique to extract TSK-type fuzzy rules from a given set of input-output data for classification problems. Fuzzy clusters are generated incrementally from the training data set, and similar clusters are merged dynamically together through input-similarity, output-similarity, and output-variance tests. The associated membership functions are defined with statistical means and deviations. Each cluster corresponds to a fuzzy IF-THEN rule, and the obtained rules can be further refined by a fuzzy neural network with a hybrid learning algorithm which combines a recursive SVD-based least squares estimator and the gradient descent method. The proposed technique has several advantages. The information about input and output data subspaces is considered simultaneously for cluster generation and merging. Membership functions match closely with and describe properly the real distribution of the training data points. Redundant clusters are combined and the sensitivity to the input order of training data is reduced. Besides, generation of the whole set of clusters from the scratch can be avoided when new training data are considered.
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Lu, Ho, and 呂赫. "Information Search Robot with Neuro-Fuzzy Approach." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/48650155968677579759.
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Chou, Yu-Chieh, and 周煜傑. "Intellignet Information Retrieval Agent with Neuro-Fuzzy Approach." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/56495200760296633877.
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碩士國立成功大學
資訊工程學系
86
Based on the neuro-fuzzy approach, we propose an intelligent software component retrieval system to serve as demonstration case of intelligent information retrieval (IR) system, which supports users for correctly retrieving desired information in personal convenience. The intelligent software component retrieval system can help users implementing their softwaresystems in rapid prototyping approach. Fuzzy information retrieval, knowledge-based system, and machine learning techniques are adopted to develop the proposed system. Thesaurus process and indexing process are two major parts in the proposed system, and two fuzzy neural networks are developed to realize these two processes. The learning ability of neural network helps the retrieval systemexecuting the dynamic adjustment task in personal thesaurus, so users can inquire the component retrieval system in their convenient representation. An encoding process and bias compensation process are activated in the thesaurus process to make system have the abilities of error typing tolerance and top-and-tail tolerance. Besides, the system is implemented and installed in a WWW site, so users can retrieve the components what they want by using the Internet Explorer conveniently.Three major modules of the proposed model, adaptive thesaurus, fuzzy indexing, and information filter, have been designed with ActiveX technology. By using these components, designers can easily build intelligent retrieval system in other application domains such as intelligent song retrieval of KTV systems, digital library retrieval system, electronic commerce purchasing system, and so on. The functions of applications can be increased and the time of development can be reduced easily by using software components.
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Huang, Li-Ming, and 黃立銘. "A Neuro-Fuzzy Approach for Multiple Human Objects Segmentation." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/52652425900675413672.
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碩士國立中山大學
電機工程學系研究所
91
We propose a novel approach for segmentation of human objects, including face and body, in image sequences. In modern video coding techniques, e.g., MPEG-4 and MPEG-7, human objects are usually the main focus for multimedia applications. We combine temporal and spatial information and employ a neuro-fuzzy mechanism to extract human objects. A fuzzy self-clustering technique is used to divide the video frame into a set of segments. The existence of a face within a candidate face region is ensured by searching for possible constellations of eye-mouth triangles and verifying each eye-mouth combination with the predefined template. Then rough foreground and background are formed based on a combination of multiple criteria. Finally, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network which is trained by a SVD-based hybrid learning algorithm. Through experiments, we compare our system with two other approaches, and the results have shown that our system can detect face locations and extract human objects more accurately.
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Wu, Fong Hsiang, and 吳逢祥. "Adaptive RSVP Buffer Control Based on Neuro-Fuzzy Approach." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/48691430713152583741.
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碩士國立成功大學
資訊工程研究所
87
This thesis proposes an adaptive RSVP buffer control scheme based on the neuro-fuzzy approach that is called RSVP Neuro-Fuzzy Buffer Control Scheme (RSVP-NFBCS). The RSVP-NFBCS controls the occupancy of the buffer by dynamically allocating bandwidth, so that it can not only to prevent the buffer from overflow and underflow but also improve the utilization of reserved bandwidth effectively. The RSVP-NFBCS is constructed by using a fuzzy neural network model with an additional reference model which is called Fuzzy Rule Generator (FRG). The FRG adaptively extracts from the training patterns fuzzy rules by the back-propagation learning algorithm with momentum (BPM). There are two different operation modes in RSVP-NFBCS; inference mode and learning mode. In the inference mode, the RSVP-NFBCS infers the required token rate by the learned fuzzy rules. In learning mode, the reference module FRG adopts BPM to learn the new fuzzy rules. In summary, the RSVP-NFBCS has advantages of adaptive fuzzy rule learning ability. According to the simulation results, the proposed RSVP-NFBC has a good performance of buffer control in both VBR traffic and CBR traffic.
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"A BEHAVIOR BASED ROBOT CONTROL SYSTEM USING NEURO-FUZZY APPROACH." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/109765/index.pdf.
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Leong, Kit-weng, and 梁杰榮. "A Study on Classification Problem using Complex Neuro-Fuzzy Approach." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/tu322j.
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碩士國立中央大學
資訊管理學系
103
We present a complex neuro-fuzzy system (CNFS) as a pattern classifier that utilizes complex fuzzy sets. For feature selection of training samples, we consider the removal of redundant and irrelevant features by which we aspire to improve the predictive accuracy of the classifier. Based on information theory, we employ a well-known feature selection method that combines minimal redundancy and maximal relevance for feature selection. One crucial problem for fuzzy-rule based model construction is that the amount of data is usually large in volume, which would make the consequence part parameters of rule base grow exponentially. A modified grid-partitioning method that can select portioned area of input space if some rule-firing-strength threshold is satisfied is employed to deal with that major problem. For the parameter learning method, the particle swarm optimization algorithm (PSO) and the recursive least-squares estimator (RLSE) are integrated as a hybrid learning method to adjust the free parameters of the CNFS effectively. We conducted experiments using 10 data sets of various fields and made performance comparison with other classifiers. The experimental results demonstrate that our approach can find smaller size feature subset with high classification accuracy.
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"Strategic groups: a resource-based view and neuro-fuzzy systems approach." Tese, MAXWELL, 2004. http://www.maxwell.lambda.ele.puc-rio.br/cgi-bin/db2www/PRG_0991.D2W/SHOW?Cont=5856:pt&Mat=&Sys=&Nr=&Fun=&CdLinPrg=pt.
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Kao, Chien-Jen, and 高堅仁. "A Neuro-Fuzzy Approach to System Identification and Time Series Prediction." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/93126081317828674676.
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碩士淡江大學
資訊工程研究所
83
Neural Networks are currently used extensively to find solutions to certain kinds of problems that can not be efficiently solved by means of conventional algorithms. Neural Networks widely applied are known as backpropagation networks. However, backpropagation networks suffer from lengthy training time. Furthermore, it is difficult to physically interpret the results obtained from trained networks. This thesis proposes a neuro-fuzzy system which can overcome these limitations. The neuro-fuzzy system under consideration is implemented as a two- layer Fuzzy Hyperrectangular Composite Neural Network (FHRCNN). A special hybrid training algorithm is developed to find a set of appropriate initial weights in order to speed up the learning process. First we divide the output space into fuzzy regions, and then transform function approximation into a pattern recognition problem. In this step, we use the supervised decision directed learning (SDDL) algorithm to find the information imbedded in the training data. The hidden nodes of the FHRCNN are then initialized according to the extracted information. We may use the least mean squared error (LMS) algorithm or the backpropagation algorithm to minimize the error to an acceptable value. After sufficient training, the fuzzy neural network can evolve automatically to acquire a set of fuzzy if-then rules. Based on the experimental results we conclude that the proposed neuro-fuzzy approach is an attractive alternative to traditional techniques as a tool for system identification and time series prediction.
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Neagu, Daniel, and V. Palade. "A Neuro-Fuzzy Approach for Functional Genomics Data Interpretation and Analysis." 2003. http://hdl.handle.net/10454/2630.
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Lu, Wei-Zhe, and 呂維哲. "A Neuro-fuzzy-based Approach to the Classification of Remotely Sensed Images." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/91825898402484058455.
Full textAbstract:
碩士國立中央大學
資訊工程研究所
90
Remotely sensed images offer much information on planning or exploitation of natural resources, monitoring environmentally sensitive areas, detecting sudden changes of areas, etc. Over the years, an extremely large volume of remotely sensed images is currently available. Although human interpreters often are superior in identifying land-cover/land-use on remotely sensed images, they may be overwhelmed by the amount of data. Therefore, a substantial part of these images is not optimally used because it has not been properly indexed. For this reason, it is necessary to develop a technique to automatically classify remotely sensed images. In this thesis, we first report the application of a class of HyperRectangular Composite Neural Networks (HRCNNs) for classification of remotely sensed multi-spectral image data. After sufficient training, the classification knowledge embedded in the numerical weights of trained HRCNNs can be successfully extracted and represented in the form of If-Then rules. These extracted rules are helpful to justify their responses so the classification results can be more trustable. In addition, we propose a new class of classifiers called Modified SFAM (MSFAM). MSFAM is a modified and simplified version of the well-known Fuzzy ARTMAP. Two sets of remotely sensed images are used to verify the performance of the two different classes of classifiers.
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Chun, Chia-Hao, and 莊家豪. "Corporate Governance and the Prediction of Litigation Presence- A Neuro-Fuzzy Approach." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/50696733275342759048.
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碩士國立中興大學
會計學研究所
93
This study examines if corporate governance mechanisms of publicly listed companies may play the role of self-supervision, hence provide auditors with judgmental assistances for decision-making. A sample including 62 sued cases selected from Securities and Futures Bureau as well as Investors Protcetion Center and 124 non-sued companies chosen as matched-pair samples having equivalently demographic characteristics of size and industry is used to analyze the characteristics and weaknesses of contemporary corporate governance. The study applies a logistic regression and a neuro-fuzzy technique to construct litigation-presence warning models, subsequently to capture the relationship between corporate governance and litigation presence. Empirical results show that litigation presence significantly has negative relation with both the shareholding and the number of directors and supervisors. However, the relationship between institutional/secondary shareholders and litigation presence remains unclear. Further, concerning the prediction ability, the logistic regression can provide the earliest warnings in comparison with neuro-fuzzy, but such an ability would be violated if structural changes occur, for instance, law regulations become rigorous. On the contrary, the neuro-fuzzy with its unique ability of learning offers better warning while the time is getting closed to litigation occurrence. Hence benefits to the related parties could be derived from avoiding economic losses and resource wastes. In addition, the knowledge base rules and 3D plots among the variables obtained from the neuro-fuzzy also offer a promotion of auditing effectiveness and efficiency, and a guidance for regulation establishments.
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Chi-Hong, Chen. "A Final Price Prediction Model for online English Auctions -A Neuro-Fuzzy Approach." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0005-1307200611455200.
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Chen, Chi-Hong, and 陳志弘. "A Final Price Prediction Model for online English Auctions—A Neuro-Fuzzy Approach." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/41638647161178117822.
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碩士國立中興大學
電子商務研究所
94
Markov Chain Model provides a concise mathematical model to describe the online English auction process, converting the complicated interaction between the bidders and auctioneer into a tractable mathematical problem, which is a milestone for researches involved in this area. However, the assumptions about the parameters are not consistent with the actual phenomena, for example, the distribution of the private values and the arrival rates. Furthermore it is hard to obtain the values of these parameters. In this research, a hybrid method, Neuro-Fuzzy, is proposed to predict the final price in addition to exploring the complicated, possibly nonlinear, relationship between the auction mechanisms and final price. The research results show that Neuro Fuzzy system can predict the final price accurately much better than the others, which is of great help for the buyers to avoid overpricing and for the sellers to facilitate the auction. Besides, the knowledge base obtained from Neuro Fuzzy provides the elaborative relationship among the variables, which can be further tested for theory building.
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Chen, Jian-Sin, and 陳建欣. "The performance study of price-quantity based trading system － A neuro fuzzy approach." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/63854817802975693167.
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碩士靜宜大學
企業管理研究所
90
Weak form market efficiency implies that stock price has contained the related information in the past that investors cannot get excess return through the technical analysis. In this case, investors can only diversify the risk through a portfolio strategy. Owing to its importance, the weak form market efficiency has been a focus no matter in the academia or practice for years. Price and quantity are two important variables in technical analysis. Ying(1966)、Copeland(1976)、Epps(1975) and Smirlock & Starks(1985) revealed that quantity of stock is positively related to the absolute value of price change. Some researches also emphasized the existence of linear relationship among price, quantity, and next day’s price change. However, the nonlinear relationships are rarely referred to. This paper assumes that the relationships among the variables are complicated more than just linear relationship, and tries to capture the nonlinear relationships by using a hybrid technique—neuro fuzzy. Moreover constructing a price-quantity based trading system to probe with Weak Form Efficient. The objective of this paper is combining price-quantity technical index and neuro-fuzzy hybrid technique to construct a trading system for each Morgan stocks. Another we also compare the portfolio performance constructed by this proposed trading system with the performance of Markowitz model. The empirical results show that the proposed model beats the market in return of year and sharpe ratio. It is also right in different market condition. When the trading cost increases, the return of neuro-fuzzy is eroded. Another proposed model beats the market in return of day and each of trading is profitable. The four kinds of excess return index are all positive. The return and sharpe ratio of portfolio is better than Markowitz, and all better than other indexes.
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LY, NGUYEN THI HA, and 阮氏荷莉. "Adaptive Neuro-Fuzzy Predictive Control Approach for Design of Cooperative Adaptive Cruise Control System." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/94upa4.
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Tettey, Thando. "A computational intelligence approach to modelling interstate conflict : Forecasting and causal interpretations." Thesis, 2008. http://hdl.handle.net/10539/5863.
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The quantitative study of conflict management is concerned with finding models
which are accurate and also capable of providing a causal interpretation of results.
This dissertation applies computational intelligence methods to study interstate disputes.
Both multilayer perceptron neural networks and Takagi-Sugeno neuro-fuzzy
models are used to model interstate interactions. The multilayer perceptron neural
network is trained in the Bayesian framework, using the Hybrid Monte Carlo method
to sample from the posterior probabilities. It is found that the network is able to
forecast conflict with an accuracy of 77.3%. A hybrid machine learning method using
the neural network and the genetic algorithm is then presented as a method of
suggesting how conflict can be brought under control. The automatic relevance determination
approach and the sensitivity analysis are used as methods of extracting
causal information from the neural network. The Takagi-Sugeno neuro-fuzzy model
is optimised, using the Gustafson-Kessel clustering algorithm to partion the input
space. It is found that the neuro-fuzzy model predicts conflict with an accuracy of
80.1%. The neuro-fuzzy model is also incorporated into the hybrid machine learning
method to suggest how the identified conflict cases can be avoided. The casual
interpretation is then formulated by a linguistic approximation of the fuzzy rules
extracted from the neuro-fuzzy model. The major finding in this work is that the
interpretations drawn from both the neural network and the neuro-fuzzy model are
consistent.
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Du, Shih-Huai, and 杜世懷. "A Neuro-Fuzzy Approach to Detection of Human Face and Body for MPEG Video Compression." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/06796222400390784842.
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碩士國立中山大學
電機工程學系研究所
89
For some new multimedia applications using Mpeg-4 or Mpeg-7 video coding standards, it is important to find the main objects in a video frame. In this thesis, we propose a neuro-fuzzy modeling approach to the detection of human face and body. Firstly, a fuzzy clustering technique is performed to segment a video frame into clusters to generating several fuzzy rules. Secondly, chrominance and motion features are used to roughly classify the clusters into foreground and background, respectively. Finally, the fuzzy rules are refined by a fuzzy neural network, and the ambiguous regions between foreground and background are further distinguished by the fuzzy neural network. Our method improves the correctness of human face and body detection by getting training data more precisely. Besides, we can extract the VOs correctly even the VOs have no obvious motion in the video sequence.
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Lin, Chuan-Wei, and 林傳維. "A Study for Interval Forecasting – An Intelligent Approach Using Complex Neuro-Fuzzy System, Support Vector Regression and Bootstrap." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/xh3a6a.
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碩士國立中央大學
資訊管理研究所
99
A novel intelligent approach using complex neuro-fuzzy system based support vector regression (denoted as CNFS-SVR) and moving-block bootstrap is proposed to the problem of time series interval forecasting in this thesis. The proposed CNFS-SVR approach combines both of the complex neuro-fuzzy system (CNFS) theory and the support vector regression (SVR) theory. With complex fuzzy sets (CFSs), the CNFS has excellent adaptive ability for functional mapping. The output of CNFS is complex-valued and can be used to develop the so-called dual output capability, which can be used to predict two time series simultaneously. SVR is based on the statistical learning theory. With the principle of structural risk minimization (SRM), SVR can possess excellent generalization ability without over fitting. In the study, CNFS-SVR is developed to integrate the merits of the CNFS theory and the SVR rationale to obtain excellent performance. Bootstrap is a re-sampling method, by which empirical statistical distribution can be developed and confidence interval can be obtained using statistical inference. For the learning strategy, a FCM-based clustering method is used to automatically determine the initial knowledge base of CNFS-SVR. Particle swarm optimization (PSO) and recursive least squares estimator (RLSE) algorithm are used in a hybrid way to update the parameters of If-Then fuzzy rules of CNFS-SVR. The LibSVM package is used to optimize the proposed CNFS-SVR machines. Several real-world exchange-rate time series are used in the study. The experimental results show promising performance.
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Nguyen, Huy Huynh. "A neural fuzzy approach to modeling the thermal behavior of power transformers." Thesis, 2007. https://vuir.vu.edu.au/1495/.
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This thesis presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top and bottom-oil temperatures for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. The models were derived from real data of temperature measurements obtained from two industrial power installations. A comparison of the proposed techniques is presented for predicting top and bottom-oil temperatures based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparisons of the results obtained indicate that the hybrid neuro-fuzzy network is the best candidate for the analysis and prediction of the power transformer top and bottom-oil temperatures. The ANFIS demonstrated the best comparative performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peak error.