Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants

Tobacco is an agricultural product that uses leaves to be processed into pesticides, medicines and cigarettes. Tobacco quality is determined by plant maintenance and reduced pest and disease attacks. To avoid these disturbances, control is needed quickly, precisely and accurately so that the tobacco plant disease cannot spread throughout agricultural land. In making fuzzy, diseases and symptoms in tobacco plants are used as a rule base in making a fuzzy expert system. The expert system created in this research is an expert system using the concept of fuzzy logic to diagnose tobacco plant diseases, using the Mamdani inference method and the defuzzification process using the centroid method (firmness value) to get the right conclusions in diagnosing tobacco plant diseases. From the results of Mamdani's design and manual fuzzy calculations, it can be concluded that the design is ready to be further implemented into the required programming language. From the sample calculation results, it was found that damping off disease has a moderate degree of risk with a value of 41.54. With the construction of this system, it will provide easy information for farmers to carry out and find out what symptoms are contracting diseases in tobacco plants

Neuro-fuzzy software for intelligent control and education

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 200

Modelação e controlo de sistemas com incertezas baseados em lógica difusa de tipo-2

Doutoramento em Engenharia EletrotécnicaA última fronteira da Inteligência Artificial será o desenvolvimento de um sistema computacional autónomo capaz de "rivalizar" com a capacidade de aprendizagem e de entendimento humana. Ainda que tal objetivo não tenha sido até hoje atingido, da sua demanda resultam importantes contribuições para o estado-da-arte tecnológico atual. A Lógica Difusa é uma delas que, influenciada pelos princípios fundamentais da lógica proposicional do raciocínio humano, está na base de alguns dos sistemas computacionais "inteligentes" mais usados da atualidade. A teoria da Lógica Difusa é uma ferramenta fundamental na suplantação de algumas das limitações inerentes à representação de informação incerta em sistemas computacionais. No entanto esta apresenta ainda algumas lacunas, pelo que diversos melhoramentos à teoria original têm sido introduzidos ao longo dos anos, sendo a Lógica Difusa de Tipo-2 uma das mais recentes propostas. Os novos graus de liberdade introduzidos por esta teoria têm-se demonstrado vantajosos, particularmente em aplicações de modelação de sistemas não-lineares complexos. Uma das principais vantagens prende-se com o aumento da robustez dos modelos assim desenvolvidos comparativamente àqueles baseados nos princípios da Lógica Difusa de Tipo-1 sem implicar necessariamente um aumento da sua dimensão. Tal propriedade é particularmente vantajosa considerando que muitas vezes estes modelos são utilizados como suporte ao desenvolvimento de sistemas de controlo que deverão ser capazes de assegurar o comportamento ótimo de um processo em condições de operação variáveis. No entanto, o estado-da-arte da teoria de controlo de sistemas baseada em modelos não tem integrado todos os melhoramentos proporcionados pelo desenvolvimento de modelos baseados nos princípios da Lógica Difusa de Tipo-2. Por essa razão, a presente tese propõe-se a abordar este tópico desenvolvendo uma metodologia de síntese de Controladores Preditivos baseados em modelos Takagi-Sugeno seguindo os princípios da Lógica Difusa de Tipo-2. De modo a cumprir este objetivo, quatro linhas de investigação serão debatidas neste trabalho.Primeiramente proceder-se-á ao desenvolvimento de uma metodologia de treino de Modelos Difusos de Tipo-2 simplificada, focada em dois paradigmas: manter a clareza dos intervalos de incerteza introduzidos sobre um Modelo Difuso de Tipo-1; assegurar a validade dos diversos modelos localmente lineares que constituem a estrutura Takagi- Sugeno, de modo a torná-los adequados a métodos de síntese de controladores baseados em modelos. O modelo desenvolvido é tipicamente utilizado para extrapolar o comportamento do sistema numa janela temporal futura. No entanto, quando usados em aproximações de sistemas não lineares, os modelos do tipo Takagi-Sugeno estabelecem um compromisso entre exatidão e complexidade computacional. Assim, é proposta a utilização dos princípios da Lógica Difusa de Tipo-2 para reduzir a influência dos erros de modelação nas estimações obtidas através do ajuste dos intervalos de incerteza dos parâmetros do modelo. Com base na estrutura Takagi-Sugeno, um método de linearização local de modelos não-lineares será utilizado em cada ponto de funcionamento do sistema de modo a obter os parâmetros necessários para a síntese de um controlador otimizado numa janela temporal futura de acordo com os princípios da teoria de Controlo Preditivo Generalizado - um dos algoritmos de Controlo Preditivo mais utilizado na indústria. A qualidade da resposta do sistema em malha fechada e a sua robustez a perturbações serão então comparadas com implementações do mesmo algoritmo baseadas em métodos de modelação mais simples. Para concluir, o controlador proposto será implementado num System-on-Chip baseado no core ARM Cortex-M4. Com o propósito de facilitar a realização de testes de implementação de algoritmos de controlo em sistemas embutidos, será apresentada também uma plataforma baseada numa arquitetura Processor-In-the-Loop, que permitirá avaliar a execução do algoritmo proposto em sistemas computacionais com recursos limitados, aferindo a existência de possíveis limitações antes da sua aplicação em cenários reais. A validade do novo método proposto é avaliada em dois cenários de simulação comummente utilizados em testes de sistemas de controlo não-lineares: no Controlo da Temperatura de uma Cuba de Fermentação e no Controlo do Nível de Líquidos num Sistema de Tanques Acoplados. É demonstrado que o algoritmo de controlo desenvolvido permite uma melhoria da performance dos processos supramencionados, particularmente em casos de mudança rápida dos regimes de funcionamento e na presença de perturbações ao processo não medidas.The development of an autonomous system capable of matching human knowledge and learning capabilities embedded in a compact yet transparent way has been one of the most sought milestones of Artificial Intelligence since the invention of the first mechanical general purpose computers. Such accomplishment is yet to come but, in its pursuit, important contributions to the state-of-the-art of current technology have been made. Fuzzy Logic is one of such, supporting some of the most used frameworks for embedding human-like knowledge in computational systems. The theory of Fuzzy Logic overcame some of the difficulties that the inherent uncertainty in information representations poses to the development of computational systems. However, it does present some limitations so, aiming to further extend its capabilities, several improvements over its original formalization have been proposed over the years such as Type-2 Fuzzy Logic - one of its most recent advances. The additional degrees of freedom of Type-2 Fuzzy Logic are showing greater potential to supplant its original counterpart, especially in complex non-linear modeling tasks. One of its main outcomes is its capability of improving the developed model’s robustness without necessarily increasing its dimensionality comparatively to a Type-1 Fuzzy Model counterpart. Such feature is particularly advantageous if one considers these model as a support for developing control systems capable of maintaining a process’s optimal performance over changing operating conditions. However, state-of-the art model-based control theory does not seem to be taking full advantage of the improvements achieved with the development of Type-2 Fuzzy Logic based models. Therefore, this thesis proposes to address this problem by developing a Model Predictive Control system supported by Interval Type-2 Takagi- Sugeno Fuzzy Models. To accomplish this goal, four main research directions are covered in this work.Firstly, a simpler method for training a Type-2 Takagi-Sugeno Fuzzy Model focused on two main paradigms is proposed: maintaining a meaningful interpretation of the uncertainty intervals embedded over an estimated Type-1 Fuzzy Model; ensuring the validity of several locally linear models that constitute the Takagi-Sugeno structure in order to make them suitable for model-based control approaches. Based on the developed model, a multi-step ahead estimation of the process behavior is extrapolated. However, as Takagi-Sugeno Fuzzy Models establish a trade-off between accuracy and computational complexity when used as a non-linear process approximation, it is proposed to apply the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on the obtained estimations by adjusting the model parameters’ uncertainty intervals. Supported by the developed Type-2 Takagi-Sugeno Fuzzy Model, a locally linear approximation of each current operation point is used to obtain the optimal control law over a prediction horizon according to the principles of Generalized Predictive Control - one of the most used Model Predictive Control algorithms in Industry. The improvements in terms of closed loop tracking performance and robustness to unmodeled operation conditions are then assessed comparatively to Generalized Predictive Control implementations based on simpler modeling approaches. Ultimately, the proposed control system is implemented in a general purpose System-on-a-Chip based on a ARM Cortex-M4 core. A Processor-In-the-Loop testing framework, developed to support the implementation of control loops in embedded systems, is used to evaluate the algorithm’s turnaround time when executed in such computationally constrained platform, assessing its possible limitations before deployment in real application scenarios. The applicability of the new methods introduced in this thesis is illustrated in two simulated processes commonly used in non-linear control benchmarking: the Temperature Control of a Fermentation Reactor and the Liquid Level Control of a Coupled Tanks System. It is shown that the developed control system achieves an improved closed loop performance of the above mentioned processes, particularly in the cases of quick changes in the operation regime and in presence of unmeasured external disturbances

Entwicklung eines auf Fuzzy-Regeln basierten Expertensystems zur Hochwasservorhersage im mesoskaligen Einzugsgebiet des Oberen Mains

People worldwide are faced with flood events of different magnitudes. A timely and reliable flood forecast is essential for the people to save goods and, more important, lives. The development of a fuzzy rule based flood forecast system considering extreme flood events within meso-scale catchments and with return periods of 100 years and more is the main objective of this work. Considering one river catchment extreme flood events are usually seldom. However, these data are essential for a reliable setup of warning systems. In this work the database is extended by simulations of possible flood events performing the hydrological model WaSiM-ETH (Water balance Simulation model ETH) driven by generated precipitation fields. The therefore required calibration of the hydrological model is performed applying the genetic optimization algorithm SCE (Shuffled Complex Evolution). Thereby, different SCE configuration setups are investigated and an optimization strategy for the Upper Main basin is developed in order to ensure reliable und satisfying calibration results. In this thesis the developed forecast system comprises different time horizons (3 days; 6, 12, and 48 hours) in order to ensure a reliable and continuous flood forecast at the three main gauges of the Upper Main river. Thereby, the focus of the different fuzzy inference systems lies on different discharge conditions, which together ensure a continuous flood forecast. In this work the performance of the two classical fuzzy inference systems, Mamdani and Takagi-Sugeno, is investigated considering all four forecast horizons. Thereby, a wide variety of different input features, among others Tukey data depth, is taken into consideration. For the training of the fuzzy inference systems the SA (Simulated Annealing) optimization algorithm is applied. A further performance comparison is carried out considering the 48 hour forecast behaviour of the two fuzzy inference systems and the hydrological model WaSiM-ETH. In this work the expert system ExpHo-HORIX is developed in order to combine the single, trained fuzzy inference systems to one overall flood warning system. This expert system ensures beside the fast forecast a quantification of uncertainties within a manageable, user-friendly, and transparent framework which can be easily implemented into an exiting environment.Menschen weltweit werden mit Hochwasserereignissen unterschiedlicher Stärke konfrontiert. Um Eigentum und, noch viel wichtiger, Leben zu retten, ist eine rechtzeitige und zuverlässige Hochwasserwarnung und folglich -vorhersage unerlässlich. Ziel dieser Arbeit ist es deshalb, ein auf Fuzzy-Regeln basiertes Hochwasserwarnsystem für mesoskalige Einzugsgebiete und die Vorhersage von extremen Hochwasserereignissen mit Wiederkehrperioden von 100 Jahren und mehr unter Berücksichtigung von Unsicherheiten zu entwickeln. Da extreme Hochwasserereignisse mit einer Jährlichkeit von 100 oder mehr Jahren in der Realität nicht in jedem Einzugsgebiet bereits beobachtet und aufgezeichnet wurden, ist eine Erweiterung der Datenbank auf Grund von Modellsimulationen zwingend notwendig. In dieser Arbeit werden hierzu das hydrologische Modell WaSiM-ETH (Wasserhaushalts-Simulations-Modell ETH) sowie von Bliefernicht et al. (2008) generierte Niederschlagsfelder verwendet. Die Kalibrierung des Modells erfolgt mit dem SCE (Shuffled Complex Evolution) Optimierungsalgorithmus. Um reproduzierbare Kalibrierungsergebnisse zu erzielen und die notwendige Kalibrierungszeit möglichst gering zu halten, werden unterschiedliche Optimierungskonfigurationen untersucht und eine Kalibrierungsstrategie für das mesoskalige Einzugsgebiet des Oberen Mains entwickelt. Um eine kontinuierliche und zuverlässige Vorhersage zu garantieren, ist die Idee entwickelt worden, Fuzzy-Regelsysteme für unterschiedliche Vorhersagehorizonte (3 Tage; 6, 12 und 48 Stunden) für die drei Hauptpegel des Oberen Mains aufzustellen, die im Zusammenspiel eine kontinuierliche Vorhersage sicher stellen. Der Fokus der 3-Tagesvorhersage liegt hierbei in der zuverlässigen Wiedergabe von geringen und mittleren Abflussbedingungen sowie der zuverlässigen und rechtzeitigen Vorhersage von Überschreitungen einer vordefinierten Meldestufe. Eine vorhergesagte Überschreitung der Meldestufe führt zu einem Wechsel der Vorhersagesysteme von der 3-Tages- zu der 6-, 12- und 48-Stundenvorhersage, deren Fokus auf der Vorhersage der Hochwasserganglinie liegt. In diesem Zusammenhang wird die Effizienz der beiden klassischen Regelsysteme,Mamdani und Takagi-Sugeno, sowie die Kombination unterschiedlicher Eingangsgrößen, unter anderem Tukey Tiefenfunktion, näher untersucht. Ein weiterer Effizienzvergleich wird zwischen den Mamdani Regelsystemen der 48-Stundenvorhersage und dem hydrologischen ModellWaSiM-ETH durchgeführt. Für das Training der beiden Regelsysteme wird der SA (Simulated Annealing) Optimierungsalgorithmus verwendet. Die einzelnen Fuzzy-Regelsysteme werden schließlich in dem entwickelten Hochwasserwarnsystem ExpHo-HORIX (Expertensystem Hochwasser - HORIX) zusammengefügt. Standardmäßig wird für jede Vorhersage die Niederschlagsunsicherheit auf Grund von Ensemble-Vorhersagen innerhalb ExpHo-HORIX analysiert und ausgewiesen. Im Hochwasserfall können für die stündlichen Fuzzy-RegelsystemeModellunsicherheiten des hydrologischenModells, das für die Generierung der Datenbank von Extremereignissen verwendet wurde, zusätzlich ausgewiesen werden. Hierzu müssen zusätzlich Ergebnisse der SCEM Analyse (Grundmann, 2009) vorliegen

Inspection and Monitoring of Structural Damage Using Vibration Signatures and Smart Techniques

The structural damage detection plays an important role in the evaluation of structural systems and to ensure their safety. Structures like large bridges should be continuously monitored for detection of damage. The cracks usually change the physical parameters like stiffness and flexibility which in turn changes the dynamic properties such as natural frequencies and mode shapes. Crack detection of a beam element comprises of two aspects: the first one is the forward problem which is achieved from the Eigen parameters and the second one is the process to locate and quantify the effect of damage and is termed as ‘inverse process of damage detection’. In the present investigation the analytical and numerical methods are known as the forward problem includes determination of natural frequencies from the knowledge of beam geometry and crack dimension. The vibration signals are derived from the forward problem is exploited in the inverse problem. The natural frequency changes occur due to the various reasons such as boundary condition changes, temperature variations etc. Among all the changes boundary condition changes are the most important factors in structural elements. Many major structures like bridges are made up of uniform beams of unknown boundary conditions. So in the present investigation two of the boundary conditions i.e. fixed -free and fixed- fixed are considered. Using the forward solution method, the natural frequencies are determined. In the inverse solution method various Artificial Intelligence (AI) techniques with their hybrid methods are proposed and implemented. Damage detection problems using Artificial Intelligence techniques require a number of training data sets that represent the uncracked and cracked scenarios of practical structural elements. In the second part of the work different AI techniques like Fuzzy Logic, Genetic Algorithm, Clonal Selection Algorithm, Differential Evolution Algorithm and their hybrid methods are designed and developed. In summary this investigation is a step towards to forecast the position of the damage using the Artificial Intelligence techniques and compare their results. Finally, the results from the Artificial Intelligence techniques and their hybridized algorithms are validated by doing experimental analysis

Rule model simplification

Centre for Intelligent Systems and their ApplicationsDue to its high performance and comprehensibility, fuzzy modelling is becoming more and more popular in dealing with nonlinear, uncertain and complex systems for tasks such as signal processing, medical diagnosis and financial investment. However, there are no principal routine methods to obtain the optimum fuzzy rule base which is not only compact but also retains high prediction (or classification) performance. In order to achieve this, two major problems need to be addressed. First, as the number of input variables increases, the number of possible rules grows exponentially (termed curse of dimensionality). It inevitably deteriorates the transparency of the rule model and can lead to over-fitting, with the model obtaining high performance on the training data but failing to predict the unknown data successfully. Second, gaps may occur in the rule base if the problem is too compact (termed sparse rule base). As a result, it cannot be handled by conventional fuzzy inference such as Mamdani. This Ph.D. work proposes a rule base simplification method and a family of fuzzy interpolation methods to solve the aforementioned two problems. The proposed simplification method reduces the rule base complexity via Retrieving Data from Rules (RDFR). It first retrieves a collection of new data from an original rule base. Then the new data is used for re-training to build a more compact rule model. This method has four advantages: 1) It can simplify rule bases without using the original training data, but is capable of dealing with combinations of rules and data. 2) It can integrate with any rule induction or reduction schemes. 3) It implements the similarity merging and inconsistency removal approaches. 4) It can make use of rule weights. Illustrative examples have been given to demonstrate the potential of this work. The second part of the work concerns the development of a family of transformation based fuzzy interpolation methods (termed HS methods). These methods first introduce the general concept of representative values (RVs), and then use this to interpolate fuzzy rules involving arbitrary polygonal fuzzy sets, by means of scale and move transformations. This family consists of two sub-categories: namely, the original HS methods and the enhanced HS methods. The HS methods not only inherit the common advantages of fuzzy interpolative reasoning -- helping reduce rule base complexity and allowing inferences to be performed within simple and sparse rule bases -- but also have two other advantages compared to the existing fuzzy interpolation methods. Firstly, they provide a degree of freedom to choose various RV definitions to meet different application requirements. Secondly, they can handle the interpolation of multiple rules, with each rule having multiple antecedent variables associated with arbitrary polygonal fuzzy membership functions. This makes the interpolation inference a practical solution for real world applications. The enhanced HS methods are the first proposed interpolation methods which preserve piece-wise linearity, which may provide a solution to solve the interpolation problem in a very high Cartesian space in the mathematics literature. The RDFR-based simplification method has been applied to a variety of applications including nursery prediction, the Saturday morning problem and credit application. HS methods have been utilized in truck backer-upper control and computer hardware prediction. The former demonstrates the simplification potential of the HS methods, while the latter shows their capability in dealing with sparse rule bases. The RDFR-based simplification method and HS methods are further integrated into a novel model simplification framework, which has been applied to a scaled-up application (computer activity prediction). In the experimental studies, the proposed simplification framework leads to very good fuzzy rule base reductions whilst retaining, or improving, performance

On a class of rational matrices and interpolating polynomials related to the discrete Laplace operator

Let \dlap be the discrete Laplace operator acting on functions (or rational matrices) $f:\mathbf{Q}_L\to\mathbb{Q}$, where $\mathbf{Q}_L$ is the two dimensional lattice of size $L$ embedded in $\mathbb{Z}_2$. Consider a rational $L\times L$ matrix $\mathcal{H}$, whose inner entries $\mathcal{H}_{ij}$ satisfy \dlap\mathcal{H}_{ij}=0. The matrix $\mathcal{H}$ is thus the classical finite difference five-points approximation of the Laplace operator in two variables. We give a constructive proof that $\mathcal{H}$ is the restriction to $\mathbf{Q}_L$ of a discrete harmonic polynomial in two variables for any $L>2$. This result proves a conjecture formulated in the context of deterministic fixed-energy sandpile models in statistical mechanics.Comment: 18 pag, submitted to "Note di Matematica

Monotonicity aspects of linguistic fuzzy models

Their interpretable model structure sets linguistic fuzzy m models apart from other modelling techniques and is considered their greatest asset. Therefore, in the identification process of a linguistic fuzzy model, the interpretability of the model should be safeguarded or at least be balanced against its accuracy. A good trade-off between accuracy and interpretability can be obtained by including as much qualitative knowledge as possible in the data-driven model identification process. Monotonicity is the type of qualitative knowledge that plays a central role in this dissertation. Monotone is hereby interpreted as order-preserving. This dissertation contributes to the ecological modelling domain by the application of fuzzy ordered classifiers to a habitat suitability modelling problem of river sites along springs to small rivers in the Central and Western Plains of Europe for 86 macroinvertebrate species. Furthermore, it contributes to the fuzzy modelling domain by (1) introducing an accurate and fast computational method for determining the crisp output of Mamdani-Assilian models applying the Center of Gravity defuzzification method and using fuzzy output partitions of trapezial membership functions, (2) presenting a new performance measure for fuzzy ordered classifiers, referred to as the average deviation (AD) as it takes the ordering of the output classes into account, (3) formulating guidelines for designers of monotone linguistic fuzzy models and (4) introducing a new inference procedure, called ATL-ATM inference, for linguistic fuzzy models with a monotone rule base

Improving the cost model development process using fuzzy logic

EThOS - Electronic Theses Online ServiceGBUnited Kingdo