Economic systems analysis is a systems analysis technique of setting out the factors that have to be taken into account in making economic systems decisions. The inquiring and operational systems of the technique are almost exclusively designed for well-structured systems. In review of economic systems analysis against systems thinking, there is a growing tendency to discard the analytical approach as inappropriate for dealing with an ill-structured issue. Therefore, economic systems analysis needs both the inquiring and operational systems which are appropriate for ill-structured systems. The foregoing leads to the introduction of an extensive methodology. Mainly, the weakness of economic systems analysis methodology can be traced to the philosophical paradigm upon which the technique is based. In this study, four main aspects of both the inquiring and operational systems of economic systems analysis are being explored: (1) A new philosophical paradigm is proposed as the foundation of general methodology in place of the traditional Newtonian-Kantian inquiring system. (2) The new philosophical paradigm needs new problem formulation and analysis space; therefore, a multidimensional, synergetic, and autopoietic model is proposed for systems synthesis and systems analysis. (3) The new philosophical paradigm is characterized as a Singerian inquiry, and as a result, Marglin's multiobjective analysis is replaced by a Singerian multiobjective analysis. (4) Markov communication theory and fuzzy sets theory are proposed as tools for handling complexity. Markov communication theory and fuzzy sets theory are introduced for systems design and multiple objective analysis. This study reports on the first application of a Singerian fuzzy multiobjective mathematical algorithm in economic systems analysis, concluding that fuzzy systems theory, especially Markov communication theory, can realize approximate reasoning in economic systems analysis. Fuzzy modeling offers a deeper understanding of complexity and a means of expressing the insights that result from that understanding; moreover, it provides a means of incorporating subjectivity and adaptation. Therefore, fuzzy modeling increases the validity of the systems approach for dealing with ill-structured systems. The proposed method represents an important theoretical improvement of Marglin's approach. The results, however, also hold practical importance, for they are of practical interest to systems analysts who would improve systems design and multiobjective analysis.

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This work focus on the genetic generation of fuzzy systems. One of the main contribution of this work is the proposal of the FCA-BASED method, which generates the genetic search space using the formal concept analysis theory by extracting rules from data. The experimental evaluation results of the FCA-BASED method show its robustness, producing a good trade-off between the accuracy and the interpretability of the generated models. Moreover, the FCA-BASED method presents improvements to the DOC-BASED method, a previously proposed approach, related to the reduction of the computational cost for the generation of the genetic search space. In order to tackle high dimensional datasets, we also propose the FUZZYDT method, a fuzzy version of the classic C4.5 decision tree, a highly scalable method that presents low computational cost and competitive accuracy. Due to these characteristics, FUZZYDT is used in this work as a baseline method for the experimental evaluation and comparisons of other classic and fuzzy classification methods. We also include in this work the use of the FUZZYDT method to a real world problem, the warning of the coffee rust disease in Brazilian crops. Furthermore, this work investigates the task of feature subset selection to address the dimensionality issue of fuzzy systems. To this end, we propose the FUZZYWRAPPER method, a wrapper-based approach that selects features taking the relevant information regarding the fuzzyfication of the attributes into account, in the feature selection process. This work also investigates the automatic design of fuzzy data bases, proposing the FUZZYDBD method, which estimates the number of fuzzy sets defining all the attributes of a dataset and evenly distributing the fuzzy sets in the domains of the attributes. A modified version of the FUZZYDBD method, FUZZYDBD-II, which defines independent numbers of fuzzy sets for each attribute of a dataset, by means of estimation functions, is also proposed in this work / Este trabalho foca na geração genética de sistemas fuzzy. Uma das principais contribuições deste trabalho é a proposta do método FCA-BASED, que gera o espaço de busca genético usando a teoria de análise de conceitos formais por meio da extração de regras dos dados. Os resultados da avaliação experimental do método FCA-BASED demonstram sua robustez. O método FCABASED também produz um bom trade-off entre acurácia e interpretabilidade dos modelos gerados. Além disso, o método FCA-BASED apresenta melhorias em relação ao método DOC-BASED, uma abordagem proposta anteriormente. Essas melhorias estão relacionadas à redução do custo computacional para a geração do espaço de busca genético. Para ser capaz de trabalhar com conjuntos de dados de alta dimensão, foi também proposto o método FUZZYDT, uma versão fuzzy da clássica árvore de decisão C4.5. FUZZYDT é um método altamente escalável que apresenta baixo custo computacional e acurácia competitiva. Devido a essas características, o FUZZYDT é usado nesse trabalho como um método baseline para a avaliação experimental e comparações de outros métodos de classificação, fuzzy e clássicos. Também está incluido nesse trabalho a aplicação do método FUZZYDT em um problema do mundo real, o alerta da doença da ferrugem cafeeira em plantações brasileiras. Além disso, esse trabalho investiga a tarefa de seleção de atributos como forma de atacar o problema da dimensionalidade de sistemas fuzzy. Para esse fim, foi proposto o método FUZZYWRAPPER, uma abordagem baseada em wrapper que seleciona atributos levando em consideração as informações relevantes sobre a fuzificação dos atributos durante o processo de seleção. Esse trabalho também investiga a construção automática de bases de dados fuzzy, incluindo a proposta do método FUZZYDBD, que estima o número de conjuntos fuzzy que define todos os atributos de um conjunto de dados e distribui os conjuntos fuzzy proporcionalmente nos domínios dos atributos. Uma versão modificada do método FUZZYDBD, o método FUZZYDBD-II, também é proposta nesse trabalho. O método FUZZYDBD-II define números independentes de conjuntos fuzzy para cada atributo de um conjunto de dados por meio de funções de estimação

This work focus on the genetic generation of fuzzy systems. One of the main contribution of this work is the proposal of the FCA-BASED method, which generates the genetic search space using the formal concept analysis theory by extracting rules from data. The experimental evaluation results of the FCA-BASED method show its robustness, producing a good trade-off between the accuracy and the interpretability of the generated models. Moreover, the FCA-BASED method presents improvements to the DOC-BASED method, a previously proposed approach, related to the reduction of the computational cost for the generation of the genetic search space. In order to tackle high dimensional datasets, we also propose the FUZZYDT method, a fuzzy version of the classic C4.5 decision tree, a highly scalable method that presents low computational cost and competitive accuracy. Due to these characteristics, FUZZYDT is used in this work as a baseline method for the experimental evaluation and comparisons of other classic and fuzzy classification methods. We also include in this work the use of the FUZZYDT method to a real world problem, the warning of the coffee rust disease in Brazilian crops. Furthermore, this work investigates the task of feature subset selection to address the dimensionality issue of fuzzy systems. To this end, we propose the FUZZYWRAPPER method, a wrapper-based approach that selects features taking the relevant information regarding the fuzzyfication of the attributes into account, in the feature selection process. This work also investigates the automatic design of fuzzy data bases, proposing the FUZZYDBD method, which estimates the number of fuzzy sets defining all the attributes of a dataset and evenly distributing the fuzzy sets in the domains of the attributes. A modified version of the FUZZYDBD method, FUZZYDBD-II, which defines independent numbers of fuzzy sets for each attribute of a dataset, by means of estimation functions, is also proposed in this work / Este trabalho foca na geração genética de sistemas fuzzy. Uma das principais contribuições deste trabalho é a proposta do método FCA-BASED, que gera o espaço de busca genético usando a teoria de análise de conceitos formais por meio da extração de regras dos dados. Os resultados da avaliação experimental do método FCA-BASED demonstram sua robustez. O método FCABASED também produz um bom trade-off entre acurácia e interpretabilidade dos modelos gerados. Além disso, o método FCA-BASED apresenta melhorias em relação ao método DOC-BASED, uma abordagem proposta anteriormente. Essas melhorias estão relacionadas à redução do custo computacional para a geração do espaço de busca genético. Para ser capaz de trabalhar com conjuntos de dados de alta dimensão, foi também proposto o método FUZZYDT, uma versão fuzzy da clássica árvore de decisão C4.5. FUZZYDT é um método altamente escalável que apresenta baixo custo computacional e acurácia competitiva. Devido a essas características, o FUZZYDT é usado nesse trabalho como um método baseline para a avaliação experimental e comparações de outros métodos de classificação, fuzzy e clássicos. Também está incluido nesse trabalho a aplicação do método FUZZYDT em um problema do mundo real, o alerta da doença da ferrugem cafeeira em plantações brasileiras. Além disso, esse trabalho investiga a tarefa de seleção de atributos como forma de atacar o problema da dimensionalidade de sistemas fuzzy. Para esse fim, foi proposto o método FUZZYWRAPPER, uma abordagem baseada em wrapper que seleciona atributos levando em consideração as informações relevantes sobre a fuzificação dos atributos durante o processo de seleção. Esse trabalho também investiga a construção automática de bases de dados fuzzy, incluindo a proposta do método FUZZYDBD, que estima o número de conjuntos fuzzy que define todos os atributos de um conjunto de dados e distribui os conjuntos fuzzy proporcionalmente nos domínios dos atributos. Uma versão modificada do método FUZZYDBD, o método FUZZYDBD-II, também é proposta nesse trabalho. O método FUZZYDBD-II define números independentes de conjuntos fuzzy para cada atributo de um conjunto de dados por meio de funções de estimação

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010 / The latest technological trend in manufacturing worldwide is automation. Reducing human labour by using robots to do the work is purely a business decision. The reasons for automating a plant include: Improving productivity Reducing labour and equipment costs Reducing product damage Monitoring system reliability Improving plant safety. The use of robots in the automation sector adds value to the production line because of their versatility. They can be programmed to follow specific paths when moving material from one point to another and their biggest advantage is that they can operate for twenty-four hours a day while delivering consistent quality and accuracy. Vision-Guided Robots (VGRs) are developed for many different applications and therefore many different combinations of VGR systems are available. All VGRs are equipped with vision sensors which are used to locate and inspect various objects. In this study a robot and a vision system were combined for a pick-and-place application. Research was done on the design of a robot for locating, inspecting and picking selected components from a moving conveyor system.

Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. First, the support-vector based regression is used to identify initial parameter values of the consequent part of type-1 and interval type-2 fuzzy systems. Second, an overlapping clustering concept is used to derive interval valued parameters of the premise part of the type-2 fuzzy system. Publicly available peptide binding affinity data sets obtained from the literature are used in the experimental studies of this thesis. First, the proposed models are blind validated using the peptide binding affinity data sets obtained from a modelling competition. In that competition, almost an equal number of peptide sequences in the training and testing data sets (89, 76, 133 and 133 peptides for the training and 88, 76, 133 and 47 peptides for the testing) are provided to the participants. Each peptide in the data sets was represented by 643 bio-chemical descriptors assigned to each amino acid. Second, the proposed models are cross validated using mouse class I MHC alleles (H2-Db, H2-Kb and H2-Kk). H2-Db, H2-Kb, and H2-Kk consist of 65 nona-peptides, 62 octa-peptides, and 154 octa-peptides, respectively. Compared to the previously published results in the literature, the support vector-based type-1 and support vector-based interval type-2 fuzzy models yield an improvement in the prediction accuracy. The quantitative predictive performances have been improved as much as 33.6\% for the first group of data sets and 1.32\% for the second group of data sets. The proposed models not only improved the performance of the fuzzy system (which used support vector-based regression), but the support vector-based regression benefited from the fuzzy concept also. The results obtained here sets the platform for the presented models to be considered for other application domains in computational and/or systems biology. Apart from improving the prediction accuracy, this research study has also identified specific features which play a key role(s) in making reliable peptide binding affinity predictions. The amino acid features "Polarity", "Positive charge", "Hydrophobicity coefficient", and "Zimm-Bragg parameter" are considered as highly discriminating features in the peptide binding affinity data sets. This information can be valuable in the design of peptides with strong binding affinity to a MHC I molecule(s). This information may also be useful when designing drugs and vaccines.

Dissertation (PhD)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: A standard fuzzy logic controller is not robust enough to guarantee consistent closed-loop performance for highly non-linear plants. A finely tuned closed-loop response loses relevance as the system dynamics change with operating conditions. The self-adaptive fuzzy logic controller can track changes in the system parameters and modify the controller parameters accordingly. In most cases, self-adaptive fuzzy logic controllers are complex and rely on some form of mathematical plant model. The multi-mode fuzzy logic controller extends the working range of a standard fuzzy logic controller by incorporating knowledge of the non-linear system dynamics into the control rule-base. The complexity of the controller and difficulty in finding control rules have limited the application of multi-mode fuzzy logic controllers. An automated design algorithm is proposed for the design of a multi-mode control rule-base using qualitative plant knowledge. The design algorithm is cost function-based. The closed-loop response, local to a domain of the non-linear state space, can be tuned by manipulation of the cost function weights. Global closed-loop response tuning can be done by manipulation of the controller input gains. Alternatively, a self-learning or self-adaptive algorithm can be used in a model reference adaptive control architecture to optimise the control rule-base. Control rules responsible for unacceptable closed-loop performance are identified and their consequences modified. The validity of the proposed design method is evaluated in five case studies. The case studies illustrate the advantages of the multi-mode fuzzy logic controller. The results indicate that the proposed self-adaptive algorithm can be used to optimise a rule-base given a required closed-loop specification. If the system does not conform to the model reference adaptive architecture then the intuitive nature of the cost function based design algorithm proves to be an effective method for rule-base tuning. / AFRIKAANSE OPSOMMING: Standaard wasige logika beheerders is nie noodwendig robuust genoeg om goeie geslote lus werkverrigting vir hoogs nie-liniere aanlegte te waarborg nie. In Perfek ge-optimeerde beheerder se geslote lus werkverrigting mag verswak indien die aanleg-parameters weens bedryfstoestande verander. Self-aanpassende beheerders kan die verandering in die aanleg-parameters volg en die beheerder dienooreenkomstig optimeer. As In reël is In self-aanpassende beheerder kompleks en afhanklik van In wiskundige model van die aanleg. Die multi-modus wasige logika beheerder vergroot die werksbereik van die standaard wasige logika beheerder deur kennis aangaande die stelsel se bedryfstoestand en stelselparameters in die reël-basis in te bou. Die aanwending van die multi-modus beheerder word tans beperk deur die struktuur kompleksiteit en moeilike optimering van die reël-basis. In Ge-outomatiseerde multi-modus reël-basis ontwerps-algoritme wat gebruik maak van kwalitatiewe kennis van die aanleg en In kostefunksie word in hierdie proefskrif voorgestel. Die geslote lus gedrag beperk tot In gebied in die toestands-ruimte kan ge-optimeer word deur die kostefunksie gewigte te manipuleer. Die globale werkverrigting kan ge-optimeer word met die beheerder intree aanwinste. In Self-aanpassende algoritme in In model-verwysings aanpassende argitektuur word as altematieftot reël-basis optimering voorgestel. Reëls verantwoordelik vir swak werkverrigting word ge-identifiseer en verbeter deur modifikasie van die reëls se gevolgtrekkings. Die voorgestelde ontwerps-metode word deur middel van vyf gevallestudies ondersoek. Die studies dui die voordele van die multi-modus struktuur aan. Die self-aanpassende argitektuur is In kragtige hulpbron om In reël-basis te optimeer vir In gegewe geslote lus spesifikasie. Hierdie proefskrif toon aan dat indien die stelsel nie aan die vereistes van In model verwysingstelsel voldoen nie, is die kostefunksie benadering tot reël-basis ontwerp In aantreklike en intuïtief verstaanbare opsie om die reël-basis te optimeer.

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

D.Ing. / Measuring the efficiency of water utilities has been a constant challenge to various stakeholders in the water sector. There are several factors that influence the efficiency of utilities. The following study examines the different factors and establishes a model to quantify the efficiency of water utilities using limited number of variables. It utilises Fuzzy Logic to develop the measurement model. The developed method can also be used to configure a new water utility for efficiency. In addition, the research highlights some possible imperfections in the water policies that can result in an inherent inefficiency of a water utility. The developed model can assist in setting ceiling levels for utility's water assets and labour, to ensure efficiency. The model is generic and can be applied to any country or community, and can be used to configure water utilities for the poor. The Model utilised "Matlab Fuzzy Tool Box student version 2009a" software as a tool to develop the Fuzzy Inference Engine for Utility Efficiency. The study is a contribution to the domain of knowledge of water engineering science.