Modeling and linguistic knowledge extraction from systems using fuzzy relational models

Fuzzy relational models have been widely investigated and found to be an efficient tool for the identification of complex systems. However, little attention has been given to the linguistic interpretation of these models. The use of relational models is recommended since their development follows a natural sequence based on the original ideas about fuzzy sets and fuzzy logic, involving the estimation of the relations existing between linguistic terms which have previously been defined by the user. In the present paper the problem of extracting linguistic knowledge from systems by using relational models is addressed. A new algorithm for the identification of these models which can provide analytical or numerical solutions depending on user requirements is also proposed. Examples are presented showing that both quantitative and qualitative modeling can be effectively achieved by combining the proposed methodologies for identification and extraction of linguistic knowledge from systems

Agentes inteligentes difusos: uma ferramenta híbrida para exploração de processos espaciais em zonas costeiras

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Produção.A dinâmica das interações de grupos de usuários com o meio ambiente tem se intensificado a ponto de ameaçar a disponibilidade dos recursos naturais. As previsões para as zonas costeiras, em especial, apontam para o esgotamento de recursos e para a perspectiva de superpopulação. O estudo do impacto de ações humanas nessas zonas por meio de modelos matemáticos apresenta limitações em capturar a natureza da percepção dos atores e em expressar a sua conseqüente distribuição no espaço. A presente tese propõe um modelo de simulação baseado em agentes para a análise de cenários de ocupação de zonas costeiras, a partir da modelagem da percepção espacial desses agentes, construída através de Lógica Difusa. A modelagem baseada em agentes trata-se de novo enfoque para simulações e envolve a reprodução do mundo real em um virtual, onde são conduzidos experimentos. Nesse universo virtual, cada agente é representado como uma entidade independente, capaz de agir localmente, em resposta à sua percepção, comportamento e alterações de parâmetros ambientais. A Lógica Difusa vem sendo empregada com bastante sucesso no manuseio da incerteza associada ao mundo real e permite a utilização de termos lingüísticos em sistemas computacionais. O desenvolvimento de um protótipo possibilitou a comprovação da viabilidade de aplicação do modelo em casos reais, bem como a captura de comportamento real de indivíduos em zonas costeiras. Além disso, a aplicação do modelo em um caso real demonstra o seu poder de previsibilidade e o subsídio a estudos ambientais por meio de simulação computacional, indicando um grande potencial para testes de hipóteses sobre o papel que cada indivíduo representa no funcionamento global de um sistema

Development of a hierarchical fuzzy model for the evaluation of inherent safety

Inherent safety has been recognized as a design approach useful to remove or reduce hazards at the source instead of controlling them with add-on protective barriers. However, inherent safety is based on qualitative principles that cannot easily be evaluated and analyzed, and this is one of the major difficulties for the systematic application and quantification of inherent safety in plant design. The present research introduces the use of fuzzy logic for the measurement of inherent safety by proposing a hierarchical fuzzy model. This dissertation establishes a novel conceptual framework for the analysis of inherent safety and proposes a methodology that addresses several of the limitations of the methodologies available for current inherent safety analysis. This research proposes a methodology based on a hierarchical fuzzy model that analyzes the interaction of variables relevant for inherent safety and process safety in general. The use of fuzzy logic is helpful for modeling uncertainty and subjectivities implied in evaluation of certain variables and it is helpful for combining quantitative data with qualitative information. Fuzzy logic offers the advantage of being able to model numerical and heuristic expert knowledge by using fuzzy IF-THEN rules. Safety is traditionally considered a subjective issue because of the high uncertainty associated with its significant descriptors and parameters; however, this research recognizes that rather than subjective, "safety" is a vague problem. Vagueness derives from the fact that it is not possible to define sharp boundaries between safe and unsafe states; therefore the problem is a "matter of degree". The proposed method is computer-based and process simulator-oriented in order to reduce the time and expertise required for the analysis. It is expected that in the future, by linking the present approach to a process simulator, process engineers can develop safety analysis during the early stages of the design in a rapid and systematic way. Another important aspect of inherent safety, rarely addressed, is transportation of chemical substances; this dissertation includes the analysis of transportation hazard by truck using a fuzzy logic-based approach

Hierarchical Fuzzy Relational Models: Linguistic Interpretation And Universal Approximation

Hierarchical fuzzy structures composed of a series of sub-models connected in cascade have been found to be effective tools for dealing with the dimensionality problem in fuzzy systems. This paper addresses both the problems of universal approximation and linguistic interpretation of complex systems using hierarchical fuzzy models. Fuzzy relational equations are used to implement the sub-models of a hierarchical structure which has two very important properties: It is a universal approximator and it can be converted into a completely equivalent non-hierarchical model which in turn allows the extraction of linguistic knowledge in the form of consistent fuzzy IF-THEN rules. These properties are analytically derived and their application is illustrated by means of an example.1162166Bazaraa, M.S., Shetty, C.M., (1979) Nonlinear Programming: Theory and Algorithms., , John Wiley & SonsCampbello, R.J.G.B., Amaral, W.C., Optimization of hierarchical neural fuzzy models Proc. IEEE-INNS-ENNS International Joint Conference on Neural Networks, Page (CD), Como/Italy, 2000.Campello, R.J.G.B., Amaral, W.C., Modeling and linguistic knowledge extraction from systems using fuzzy relational models (2001) Fuzzy Sets and Systems, 121, pp. 113-126Campello, R.J.G.B., Amaral, W.C., Towards true linguistic modeling through optimal numerical solutions submitted, ndChen, W., Wang, L.-X., A note on universal approximation by hierarchical fuzzy systems (2000) Information Sciences, 123, pp. 241-248Luenberguer, D.G., (1997) Optimization by Vector Space Methods., , John Wiley & SonsPedrycz, W., An identification algorithm in fuzzy relational systems (1984) Fuzzy Sets and Systems, 13, pp. 153-167Pedrycz, W., (1993) Fuzzy Control and Fuzzy Systems, , Research Studies Press/John Wiley & Sons, 2nd editionRaju, G.U., Zhou, J., Kisner, R.A., Hierarchical fuzzy control (1991) Int. J. Control, pp. 1201-1216Shimojima, K., Fukuda, T., Hasegawa, Y., Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm (1995) Fuzzy Sets and Systems, 71, pp. 295-309Wang, L.-X., Universal approximation by hierarchical fuzzy systems (1998) Fuzzy Sets and Systems, 93, pp. 223-230Wang, L.-X., Analysis and design of hierarchical fuzzy systems (1999) IEEE Trans. Fuzzy Systems, 7, pp. 617-624Wang, L.-X., Mendel, J.M., Fuzzy basis functions, universal approximation and orthogonal least squares learning (1992) IEEE Trans. Neural Networks, 3, pp. 807-814Yager, R.R., Filev, D.P., (1994) Essentials of Fuzzy Modelling and Control., , John Wiley & Son

Takagi-sugeno Fuzzy Models Within Orthonormal Basis Function Framework And Their Application To Process Control

Fuzzy models within orthonormal basis function framework (OBF Fuzzy Models) have been introduced in previous works and shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. In the present paper, it is demonstrated that the OBF Takagi-Sugeno fuzzy models previously introduced by the authors are particular realizations of a more general and interpretable formulation presented here, while being able to approximate to desired accuracy a wide class of non-linear dynamic systems. In addition, a predictive control scheme based on the linearization of these models is applied to the control of a polymerization reactor.213991404Boyd, S., Chua, L.O., Fading memory and the problem of approximating nonlinear operators with Volterra series (1985) IEEE Trans. on Circuits and Systems, 32 (11), pp. 1150-1161Camacho, E.F., Bordons, C., (1998) Model Predictive Control, , Springer-VerlagCampello, R.J.G.B., Amaral, W.C., Modeling and linguistic knowledge extraction from systems using fuzzy relational models (2001) Fuzzy Sets and Systems, 121, pp. 113-126Campello, R.J.G.B., Amaral, W.C., Favier, G., Optimal Laguerre series expansion of discrete Volterra models (2001) Proc. European Control Conference, pp. 372-377. , Porto/PortugalCampello, R.J.G.B., Meleiro, L.A.C., Amaral, W.C., Maciel Filho, R., Identification of a bioprocess using Laguerre function based models Proc. 6th World Congress on Chemical Engineering, , page CD, Melbourne/Australia, 2001Delgado, M.R., Von Zuben, F., Gomide, F.A.C., Evolutionary design of Takagi-Sugeno fuzzy systems: A modular and hierarchical approach (2000) Proc. 9th IEEE Internat. Conference on Fuzzy Systems, pp. 447-452. , Houston/USADumont, G.A., Fu, Y., Non-linear adaptive control via Laguerre expansion of Volterra kernels (1993) Int. J. Adaptive Control and Signal Processing, 7, pp. 367-382Fu, Y., Dumont, G.A., An optimum time scale for discrete Laguerre network (1993) IEEE Transactions on Automatic Control, 38 (6), pp. 934-938. , JuneGüven, M.K., Passino, K.M., Avoiding exponential parameter growth in fuzzy systems (2001) IEEE Trans. Fuzzy Systems, 9, pp. 194-199Jang, J.-S.R., ANFIS: Adaptive-network-based fuzzy inference system (1993) IEEE Trans. Systems, Man and Cybernetics, 23, pp. 665-685Ljung, L., (1999) System Identification: Theory for the User, , Prentice Hall, 2nd editionManer, B.R., Doyle F.J. III, Ogunnaike, B.A., Pearson, R.K., Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models (1996) Automatica, 32 (9), pp. 1285-1301Ninnes, B., Gustaffsson, F., Orthonormal bases for system identification (1995) Proc. 3rd European Control Conference, 1, pp. 13-18. , Rome/Italy, SeptemberOliveira, G.H.C., Amaral, W.C., Favier, G., Dumont, G.A., Constrained robust predictive controller for uncertain processes modeled by orthonormal series functions (2000) Automatica, 36 (4), pp. 563-571Oliveira, G.H.C., Campello, R.J.G.B., Amaral, W.C., Fuzzy models within orthonormal basis function framework (1999) Proc. 8th IEEE Internat. Conference on Fuzzy Systems, pp. 957-962. , Seoul/KoreaPedrycz, W., (1993) Fuzzy Control and Fuzzy Systems, , Research Studies Press/John Wiley & Sons, 2nd editionSetnes, M., Supervised fuzzy clustering for rule extraction (2000) IEEE Trans. Fuzzy Systems, 8, pp. 416-424Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans. Systems, Man and Cybernetics, SMC-15, pp. 116-132Wahlberg, B., Identification of resonant systems using Kautz filters (1991) Proc. 30th Conference on Decision and Control, pp. 2005-2010. , DecemberYager, R.R., Filev, D.P., (1994) Essentials of Fuzzy Modeling and Control, , John Wiley & Son

Control Of A Bioprocess Using Orthonormal Basis Function Fuzzy Models

Fuzzy models within the framework of orthonormal basis functions (OBF Fuzzy Models) were introduced in previous works and have shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model architectures usually adopted in the literature. In the present paper these models are reviewed and used as a basis for a predictive control scheme which is applied to the control of a process for ethyl alcohol (ethanol) production.2801806Yager, R.R., Filev, D.P., (1994) Essentials of Fuzzy Modeling and Control, , John Wiley & SonsPedrycz, W., (1993) Fuzzy Control and Fuzzy Systems, , Research Studies Press/John Wiley & Sons, 2nd editionTakagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans. Systems, Man and Cybernetics, SMC-15, pp. 116-132Babuška, R., (1998) Fuzzy Modeling for Control, , Kluwer Academic PublishersCampello, R.J.G.B., Amaral, W.C., Modeling and linguistic knowledge extraction from systems using fuzzy relational models (2001) Fuzzy Sets and Systems, 121, pp. 113-126Campello, R.J.G.B., Amaral, W.C., Towards true linguistic modeling through optimal numerical solutions (2003) International Journal of Systems Science, 34, pp. 139-157Ninness, B., Gustafsson, F., A unifying construction of orthonormal bases for system identification (1997) IEEE Trans. on Automatic Control, 42, pp. 515-521Broome, P.W., Discrete orthonormal sequences (1965) Journal of the Association for Computing Machinery, 12 (2), pp. 151-168Dumont, G.A., Fu, Y., Non-linear adaptive control via Laguerre expansion of Volterra kernels (1993) Int. J. Adaptive Control and Signal Processing, 7, pp. 367-382Back, A.D., Tsoi, A.C., Nonlinear system identification using discrete Laguerre functions (1996) Journal of Systems Engineering, 6, pp. 194-207Oliveira, G.H.C., Amaral, W.C., Favier, G., Dumont, G.A., Constrained robust predictive controller for uncertain processes modeled by orthonormal series functions (2000) Automatica, 36 (4), pp. 563-571Campello, R.J.G.B., Meleiro, L.A.C., Amaral, W.C., Maciel Filho, R., Identification of a bioprocess using Laguerre function based models (2001) Proc. 6th World Congress on Chemical Engineering, pp. CD. , Melbourne/AustraliaOliveira, G.H.C., Campello, R.J.G.B., Amaral, W.C., Fuzzy models within orthonormal basis function framework (1999) Proc. 8th IEEE Internat. Conference on Fuzzy Svstems, pp. 957-962. , Seoul/KoreaFu, Y., Dumont, G.A., An optimum time scale for discrete Laguerre network (1993) IEEE Trans. on Automatic Control, 38 (6), pp. 934-938Campello, R.J.G.B., Favier, G., Amaral, W.C., Optimal expansions of discrete-time Volterra models using Laguerre functions Automatica, , (to appear), ndCampello, R.J.G.B., Amaral, W.C., Takagi-Sugeno fuzzy models within orthonormal basis function framework and their application to process control (2002) Proc. 11th IEEE Internat. Conference on Fuzzy Systems, pp. 1399-1404. , Honolulu/USAWahlberg, B., System identification using Kautz models (1994) IEEE Trans. on Automatic Control, 39 (6), pp. 1276-1282Boyd, S., Chua, L.O., Fading memory and the problem of approximating nonlinear operators with Volterra series (1985) IEEE Trans. on Circuits and Systems, 32 (11), pp. 1150-1161Ljung, L., (1999) System Identification: Theory for the User, , Prentice Hall, 2nd editionZervos, C.C., Dumont, G.A., Deterministic adaptive control based on Laguerre series representation (1988) Int. J. Control, 48 (6), pp. 2333-2359Wahlberg, B., Ljung, L., Hard frequency-domain model error bounds from least-squares like identification techniques (1992) IEEE Trans. on Automatic Control, 37 (7), pp. 900-912Camacho, E.F., Bordons, C., (1999) Model Predictive Control, , Springer-VerlagMeleiro, L.A.C., Filho, R.M., A self-tuning adaptive control applied to an industrial large-scale ethanol production (2000) Computers and Chemical Engineering, 24 (2-7), pp. 925-930Andrietta, S.R., Maugeri, F., Optimum design of a continuous fermentation unit of an industrial plant for alcohol production (1994) Advances in Bioprocess Engineering, pp. 47-52. , E. Galindo and O. T. Ramirez, Eds., Kluwer Academic PublishersMeleiro, L.A.C., Filho, R.M., Campello, R.J.G.B., Amaral, W.C., Hierarchical neural fuzzy models as a tool for process identification: A bioprocess application (2001) Application of Neural Networks and Other Learning Technologies in Process Engineering, , I. M. Mujtaba and M. A. Hussain, Eds. Imperial College PressJang, J.-S.R., ANFIS: Adaptive-network-based fuzzy inference system (1993) IEEE Trans. Systems, Man and Cybernetics, 23, pp. 665-685Setnes, M., Supervised fuzzy clustering for rule extraction (2000) IEEE Trans. Fuzzy Systems, 8, pp. 416-424Delgado, M.R., Von Zuben, F.J., Gomide, F.A.C., Evolutionary design of Takagi-Sugeno fuzzy systems: A modular and hierarchical approach (2000) Proc. 9th IEEE Internat. Conference on Fuzzy Systems, pp. 447-452. , Houston/USAGüven, M.K., Passino, K.M., Avoiding exponential parameter growth in fuzzy systems (2001) IEEE Trans. Fuzzy Systems, 9, pp. 194-19