Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.</jats:p

A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions

Modelo fuzzy genético para a estimação de forças em correntes a partir da medição das frequências naturais

Orientador: Milton Dias JuniorDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: As instalações em alto mar possuem linhas de ancoragem, chamadas de amarras, para proporcionar estabilidade, suporte e sustentação às estruturas. Essas linhas de ancoragem são geralmente compostas por cabos, correntes e cordas de fibra sintética. Quando a solicitação de carga é alta, as linhas de ancoragem devem ser constituídas por corrente. O monitoramento da força atuando nestas correntes é vital para a confiabilidade e segurança da produção de energia. Os métodos atuais para supervisionar as cargas nas amarras são caros e têm muitas incertezas envolvidas. Nesse contexto, propõe-se uma nova metodologia para a estimativa de força em correntes através da medição de suas frequências naturais. Um sistema de inferência difuso e otimizado por um algoritmo genético foi desenvolvido para estimar da carga nas correntes. As entradas dos modelos difusos são as frequências naturais das correntes e a saída é a força estimada. As metodologias Mamdani e Sugeno foram implementadas e comparadas. Funções de pertinência triangular e gaussiana foram usadas para modelar as entradas e a saída. As regras foram definidas de acordo com as relações entre as frequências naturais e a força na corrente. Para otimizar o sistema, o algoritmo genético pode usar como dados de treinamento os resultados fornecidos por um modelo matemático ou por um conjunto de medições. O modelo matemático desenvolvido apresenta boa concordância com os dados experimentais. O modelo genético difuso foi simulado e testado, fornecendo boa precisão na estimativa da força. Finalmente, demonstrou-se que a fuzzificação não singleton pode ser uma ferramenta útil quando as entradas são ruidosasAbstract: Offshore facilities have mooring lines to provide stability, support and holding to the structures. These mooring lines are commonly made up of synthetic fiber ropes, cables and chains. When the load solicitation is high, the mooring lines must be made up of chain. The monitoring of the strength of these chains is vital for the reliability and security of the production of energy. The current methods for supervising the loads on the chains are expensive and have many uncertainties involved. In this context, it is proposed a new methodology for the force estimation in chains through the measurements of their natural frequencies. The present dissertation arises as an improvement of this approach. A fuzzy inference system optimized by a genetic algorithm is introduced to enhance the estimation of the load on the chains. The inputs of the fuzzy models are the natural frequencies of the chains and the output is the estimated force. The Mamdani and Sugeno methodologies were implemented and compared. Triangular and Gaussian membership functions were used to model the inputs and the output. The rules were set according to the relations between the natural frequencies and the force on the chain. To optimize the system, the genetic algorithm can use the results provided by a mathematical model or by a set of measurements as training data. The mathematical model has good agreement with the experimental data. The fuzzy genetic model was simulated and tested providing good accuracy in estimating the force. In addition, the non-singleton fuzzification demonstrated that can be a helpful tool when the entries are noisyMestradoMecanica de Solidos e Projeto MecanicoMestre em Engenharia Mecânica33003017CAPE

Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.</jats:p

Automatic lateral control for unmanned vehicles via genetic algorithms

It is known that the techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Vehicle Systems; ITS is a broad range of technologies and techniques that hold answers to many transportation problems. The unmannedcontrol of the steering wheel of a vehicle is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle; to reach it, information about the car state while a human driver is handling the car is taken and used to adjust, via iterative geneticalgorithms an appropriated fuzzy controller. To evaluate the obtained controllers, it will be considered the performance obtained in the track following task, as well as the smoothness of the driving carried out

Implementation, integration, and optimization of a fuzzy foreground segmentation system

Foreground segmentation is often an important preliminary step for various video processing systems. By improving the accuracy of the foreground segmentation process, the overall performance of a video processing system has the potential for improvement. This work introduces a Fuzzy Foreground Segmentation System (FFSS) that uses Mamdani-type Fuzzy Inference Systems (FIS) to control pixel-level accumulated statistics. The error of the FFSS is quantified by comparing its output with hand-segmented ground-truth images from a set of image sequences that specifically model canonical problems of foreground segmentation. Optimization of the FFSS parameters is achieved using a Real-Coded Genetic Algorithm (RCGA). Additionally, multiple central composite designed experiments used to analyze the performance of RCGA under selected schemes and their respective parameters. The RCGA schemes and parameters are chosen as to reduce variation and execution time for a set of known multi-dimensional test functions. The selected multi-dimensional test functions represent assorted function landscapes. To demonstrate accuracy of the FFSS and implicate the importance of the foreground segmentation process, the system is applied to real-time human detection from a single-camera security system. The Human Detection System (HDS) is composed of an IP Camera networked to multiple heterogeneous computers for distributed parallel processing. The implementation of the HDS, adheres to a System of Systems (SoS) architecture which standardizes data/communication, reduces overall complexity, and maintains a high level of interoperability

Employing an Enhanced Interval Approach to encode words into Linear General Type-2 fuzzy sets for Computing With Words applications

In 1996, Zadeh coined Computing With Words (CWWs) to be a methodology in which words are used instead of numbers for computing and reasoning. One of the main challenges which faced the CWWs paradigm has been modelling words adequately. Mendel has pointed out that the CWWs paradigm should employ type-2 fuzzy logic to model words. This paper proposes employing an Enhanced Interval Approach (EIA) to create Linear General Type-2 (LGT2) fuzzy sets from Interval Type-2 (IT2) fuzzy sets to encode words for CWWs applications. We have performed experiments on 18 words belonging to 3 different linguistic variables (having 6 linguistic terms each). Interval data has been collected from 17 subjects and 18 linguistic terms have been modeled with IT2 fuzzy sets using EIA. The proposed conversion approach uses several key points within the parameters of IT2 fuzzy sets to redesign the linguistic variable using LGT2 fuzzy sets. Both IT2 and LGT2 fuzzy sets have been evaluated within a CWWs Framework, which aims to mimic the ability of humans to communicate and manipulate perceptions via words. The comparison results show that LGT2 fuzzy sets can be better than IT2 fuzzy sets in mimicking human reasoning as well as learning and adaptation since the progressive Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for LGT2 based CWWs Framework converge faster and are lower than those for IT2 based CWWs Framework