Fuzzy logic systems are widely used for control, system identification, and pattern recognition problems. In order to maximize their performance, it is often necessary to undertake a design optimization process in which the adjustable parameters defining a particular fuzzy system are tuned to maximize a given performance criterion. Some data to approximate are commonly available and yield what is called the supervised learning problem. In this problem we typically wish to minimize the sum of the squares of errors in approximating the data. We first introduce fuzzy logic systems and the supervised learning problem that, in effect, is a nonlinear optimization problem that at times can be non-differentiable. We review the existing approaches and discuss their weaknesses and the issues involved. We then focus on one of these problems, i.e., non-differentiability of the objective function, and show how current approaches that do not account for non-differentiability can diverge. Moreover, we also show that non-differentiability may also have an adverse practical impact on algorithmic performances. We reformulate both the supervised learning problem and piecewise linear membership functions in order to obtain a polynomial or factorable optimization problem. We propose the application of a global nonconvex optimization approach, namely, a reformulation and linearization technique. The expanded problem dimensionality does not make this approach feasible at this time, even though this reformulation along with the proposed technique still bears a theoretical interest. Moreover, some future research directions are identified. We propose a novel approach to step-size selection in batch training. This approach uses a limited memory quadratic fit on past convergence data. Thus, it is similar to response surface methodologies, but it differs from them in the type of data that are used to fit the model, that is, already available data from the history of the algorithm are used instead of data obtained according to an experimental design. The step-size along the update direction (e.g., negative gradient or deflected negative gradient) is chosen according to a criterion of minimum distance from the vertex of the quadratic model. This approach rescales the complexity in the step-size selection from the order of the (large) number of training data, as in the case of exact line searches, to the order of the number of parameters (generally lower than the number of training data). The quadratic fit approach and a reduced variant are tested on some function approximation examples yielding distributions of the final mean square errors that are improved (i.e., skewed toward lower errors) with respect to the ones in the commonly used pattern-by-pattern approach. Moreover, the quadratic fit is also competitive and sometimes better than the batch training with optimal step-sizes, thus showing an improved performance of this approach. The quadratic fit approach is also tested in conjunction with gradient deflection strategies and memoryless variable metric methods, showing errors smaller by 1 to 7 orders of magnitude. Moreover, the convergence speed by using either the negative gradient direction or a deflected direction is higher than that of the pattern-by-pattern approach, although the computational cost of the algorithm per iteration is moderately higher than the one of the pattern-by-pattern method. Finally, some directions for future research are identified. / Ph. D.

Accurately simulating human motions is a major function of and challenge to digital human models and integrating humans in computer-aided design systems. Numerous successful applications of human motion simulation have already demonstrated their ability to improve occupational efficiency, effectiveness, and safety. In this dissertation, a novel motion simulation model using fuzzy logic control is presented. This model was motivated by the fact that humans use linguistic terms to guide their behaviors while fuzzy logic provides mathematical representations of linguistic terms. Specifically in this model, fuzzy logic was used to specify a neural controller which was generally considered as the part in the postural control system that plans human motions. Fuzzy rules were generated according to certain trends observed from actual human motions. An optimization procedure was performed to specify the parameters of the membership functions by minimizing the differences between the simulated and actual final postures. This research contributed to the field of human movement science by providing a motion simulation model that can accurately predict novel human motions and provide interpretations of potential human motion planning strategies. Understanding balance control is another research focus in this dissertation. Investigating balance control may aid in preventing unnecessary fall-related incidents and understanding the postural control system. Since human behaviors are generally effective and efficient, balance control models (both two- and three-dimensional) based on an optimal control strategy were developed to aid in better understanding balance control. Specifically, the neural controller was considered as an optimal controller that minimizes a performance index defined by physical quantities relevant to sway. Free model parameters, such as weights of relevant physical quantities and sensory delay time, were determined by an optimization procedure whose objective was to minimize a scalar error between simulated and experimental center-of-pressure (COP) based measures. Many factors, such as aging, localized muscle fatigue, and external loads, have been found to adversely affect balance control. At the same time, behaviors during upright stance are commonly characterized by COP-based measures. Thus, changes in COP based measures with aging, LMF, and external loads were addressed by using the proposed models, and possible postural control mechanisms were identified by interpreting these changes. Findings from these studies demonstrated that the proposed models were able to accurately simulate human sway behaviors and provide plausible mechanisms regarding how the postural control system works when maintaining upright balance. / Ph. D.

Security has been widely recognized as one of the main obstacles to the adoption of Internet banking and it is considered an important aspect in the debate over challenges facing internet banking. The performance evaluation of e-banking websites requires a model that enables us to analyze the various imperative factors and criteria related to the quality and performance of e-banking websites. Ebanking site evaluation is a complex and dynamic problem involving many factors, and because of the subjective considerations and the ambiguities involved in the assessment, Fuzzy Logic (FL) model can be an effective tool in assessing and evaluating of e-banking security performance and quality. In this paper, we propose an intelligent performance assessment model for evaluating e-banking security websites. The proposed model is based on FL operators and produces four measures of security risk attack dimensions: direct internal attack, communication tampering attack, code programming attack and denial of service attack with a hierarchical ring layer structure. Our experimental results show that direct internal attack risk has a large impact on e-banking security performance. The results also confirm that the risk of direct internal attack for e-banking dynamic websites is doubled that of all other attacks.

A novel approach for estimating alertness levels from speech and tagging them with a reliability component has been developed. The Fatigue Quotient and Believability are both derived from the time series analysis of the speech signal in the communication between the operator and dispatch. Operator attention is the most important human factor element for safe transportation operations. In addition to substance abuse, illness and intoxication fatigue is a major contributing factor to the decrease of attention. The goal of this study was to develop a means to detect and estimate fatigue levels of railroad operating personnel during on-duty hours. This goal continues to gain importance with new efforts from the government to expand rail transportation operations as a tool for high speed mass transportation in urban areas. Previous research has shown that sleeping disorders, reduced hours of rest and disrupted circadian rhythms lead to significantly increased fatigue levels which manifest themselves in alterations of speech patterns as compared to alert states of mind. In this study vocal indicators of fatigue are extracted from the speech signal and Fuzzy Logic is used to generate an estimate of the cognitive state of the train conductor. The output is tagged with a believability metric based on its behavior with respect to previous outputs and a fully alert state. Communication between the conductor and dispatch over radio provides an unobtrusive way of accessing the speech signal through existing speech infrastructure. The speech signal is discretized and processed through a digital signal processing algorithm, which extracts speech metrics from the signal that were determined to be indicative of fatigue levels. Speech metrics include, but are not limited to, speech duration, silence duration, word production rate, phrase gap duration, number of words per phrase and speech intensity. A fuzzy logic minimum inference engine maps the inputs to an output through an empirically determined rule base. The rule base and the associated membership functions were derived from batch mode and real time testing and the subsequent tuning of parameters to refine the detection of changes in patterns. To increase the validity and transparency of the output time series analysis is used to create the believability metric. A moving average filter eliminates the short term fluctuations and determines the long term trend of the output. A moving standard deviation estimation quantifies instantaneous fluctuations and provides a measure of the difference to a nominal alertness state. A real time version of the algorithm was developed and prototyped on a generic, low cost and scalable hardware platform. Rapid Prototyping was realized through the Matlab/Simulink xPC Target toolbox which allowed for instant real time code generation, testing and modification. This testing environment together with batch mode testing was used to extensively test and fine tune parameters to improve the performance of the algorithm. A testing procedure was developed and standardized to collect data and tune the parameters of the algorithm. As a high level goal it was proven that the concept of digital signal processing and Fuzzy Logic can be utilized to detect changes in speech and estimate alertness levels from it. Furthermore, this study has proven that the framework to run such an analysis continuously as a monitoring function in locomotive cabins is feasible and can be realized with relatively inexpensive hardware. The development, implementation and testing process conducted for this project is explained and results are presented here. / Ph. D.

This research will compare the transient response characteristics of a four-degree-of-freedom, roll-plane model, representing a class 8 truck, using passive and semiactive dampers. The semiactive damper control policies that are examined include the previously developed policies of on-off skyhook, continuous skyhook, and on-off groundhook control, along with a newly developed method of fuzzy logic semiactive control. The model input will include body forces and torques, as well as transient displacements at the tires. The model outputs include the vehicle body heave and roll displacements, the vertical displacement of the tire (wheel hop) and the vertical acceleration of the vehicle body. For each output, the maximum peak-to-peak and RMS values of the response are examined. The results of the study show that semiactive dampers have minimal effect on improving the vehicle body and tire transients due to forces or torques applied to the body, as compared to passive dampers. For road inputs, however, semiactive dampers are able to provide a more favorable compromise between the body and axle transient dynamics, when compared to passive dampers. The fuzzy logic semiactive control policy that is proposed in this research is better able to balance the body and axle dynamics than the conventional semiactive damping control policies that are investigated. Further research on the application of fuzzy logic semiactive control concepts is suggested, in order to fully investigate the potential of such control schemes for vehicle suspensions. / Master of Science

Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of "survival of the fittest". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process. / Master of Science

The thesis examines the design and implementation of a supervisory controller for the energy management of an AC stand-alone microgrid. The microgrid under study consists of a photovoltaic (PV), battery energy storage system (BESS) and auxiliary (micro gas turbine) units connected to a common AC bus and supplies a local load. The BESS unit has to maintain the AC bus voltage and frequency and needs to balance the difference between the intermittent PV power and that consumed by the load. However, the BESS has limited energy capacity and power rating and therefore it is important to implement a supervisory controller that can curtail the PV power to prevent the battery from being overcharged and also to operate the auxiliary unit to prevent the battery from being over discharged. A Fuzzy Logic Controller (FLC) that can be implemented inside the BESS unit is proposed. It monitors the battery power and State of Charge (SOC) and varies the bus frequency accordingly. The variation in the bus frequency serves as a communication means to the PV and auxiliary units. If the frequency is increased above the nominal value, the PV unit starts to curtail its power and if the frequency is decreased, the auxiliary unit starts to generate power. Power curtailment and supplement are proportional to the frequency variation. In order to avoid any need for communication links between the units, the DC/AC inverters of all the units adopt the well-known wireless droop technique. The droop control of the auxiliary unit is implemented in such a way that the unit is floating on the bus and thus it generates power only if the bus frequency is decreased below its nominal value. The main merits of the proposed controller are simplicity and easiness of implementation inside the BESS unit. The effectiveness of the controller in protecting the battery from over-charging/over-discharging has been verified by simulations including a real-time simulation and experimentally. Furthermore, the thesis investigates the effect of sudden shading of a PV and concentrated PV (CPV) on the bus frequency of an AC stand-alone microgrid. It is known that the CPV power can drop drastically, compared to traditional PV, when it is exposed to shading. A simulation model of the CPV in a microgrid has been built and the results are compared to those of the traditional PV. It is found that shading of the CPV has much more stronger effect on the bus frequency.

Submitted by Aline Batista (alinehb.ufpel@gmail.com) on 2018-04-18T14:21:35Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_Rosana_Medina_Zanotelli.pdf: 897398 bytes, checksum: 53b6f15a010386648223058dfc32101b (MD5) / Approved for entry into archive by Aline Batista (alinehb.ufpel@gmail.com) on 2018-04-19T14:42:08Z (GMT) No. of bitstreams: 2 Dissertacao_Rosana_Medina_Zanotelli.pdf: 897398 bytes, checksum: 53b6f15a010386648223058dfc32101b (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-04-19T14:42:21Z (GMT). No. of bitstreams: 2 Dissertacao_Rosana_Medina_Zanotelli.pdf: 897398 bytes, checksum: 53b6f15a010386648223058dfc32101b (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2015-04-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Esta dissertação contribui com a análise da robustez na Lógica Fuzzy, como uma importante fundamentação para modelagem e desenvolvimento de sistemas robustos, estendendo esta abordagem para a lógica intuicionista de Atanassov. Primeiramente, apresenta-se uma introdução à lógica fuzzy, discutindo as negações, funções de agregações, implicações e coimplicações fuzzy, incluindo também os conectivos Xor e derivações. O trabalho também considera a análise da -sensibilidade destes conectivos fuzzy e suas construções duais, essencialmente focados em propriedades algébricas e projeções. Começando com a avaliação da sensibilidade de conectivos fuzzy, a proposta estende os resultados para classes de conectivos fuzzy intuicionistas. Como principal contribuição, formalmente estabelece-se que a robustez preserva as construções duais e as funções de projeção relacionadas a conectivos fuzzy intuicionistas representáveis. Mostra-se que a extensão, do trabalho científico proposto por Y. Li e colaboradores, 2005 em "An Approach to Measure the Robustness of Fuzzy Reasoning", para a classe de conectivos fuzzy intuicionistas é preservada pelas construções duais. A presente pesquisa mostra que a análise de robustez pode ser diretamente verificada a partir de operadores fuzzy usando duas estratégias: (i) a -sensibilidade de operadores fuzzy baseada na análise da monotonicidade de seus argumentos (negações, agregações, implicações e coimplicações); e ainda (ii) a avaliação do comportamento dos operadores fuzzy nos pontos terminais do intervalo unitário, onde a monotonicidade não pode ser aplicada (conectivos Xor, XNor, bi-implicações e bi-coimplicações). / This dissertation contributes to the robustness analysis in fuzzy logic as an important founding for modeling and developing robust systems, extending such approach to the Atanassov’s intuitionistic fuzzy sets. It begins with the introduction of fuzzy logic, discussing negations, aggregation functions, fuzzy implications and bi-implications, also including Xor connectives and derivations. It also considers the -sensitivity analysis of these fuzzy connectives and their dual constructions from a few essentials such as properties and projections. Starting with an evaluation of the sensitivity in representable fuzzy connectives, the results are applied in the intuitionistic connective classes. The main result formally states that the robustness preserves the projection functions of representable intuitionistic fuzzy connectives. It shows an extension of the work in Y. Li et al, 2005 "An Approach to Measure the Robustness of Fuzzy Reasoning" for the class of intuitionistic fuzzy connectives, showing that the robustness of intuitionistic fuzzy sensitivity is preserved by dual constructions. The present research states that robustness analysis of intuitionistic fuzzy operators can be directly verified from fuzzy operators using two strategies: (i) the -sensitivity analysis of fuzzy connectives based on monotonicity of their arguments (negations, aggregations, implications and coimplications); and, otherwise (ii) the evaluation of the behaviour related to fuzzy connectives in endpoints of the unit interval, when the monotonicity property is not applied (Xor, XNor, bi-implications e bi-coimplications fuzzy operators).

In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system. In order to make the type-2 fuzzy logic system reasonable and reliable, a new simple and novel statistical method to decide interval-valued fuzzy membership functions and a new probability type reduced reasoning method for the interval-valued fuzzy logic system are proposed in this thesis. In order to optimize this particle system’s performance, we adopt genetic algorithm (GA) to adjust parameters. The applications for the new system are performed and results have shown that the developed method is more accurate and robust to design a reliable fuzzy logic system than type-1 method and the computation of our proposed method is more efficient.

This diploma thesis deals with the evaluation of security companies for ZETOR TRACTORS a.s. using fuzzy logic models. The main part of the thesis consists of proposals for the evaluation of the suppliers' evaluation of the company. Decision models are created in Microsoft Excel and Matlab. Another part of the thesis deals with analysis and comparison of results from both programs.