A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition

Early Prediction of Diabetes Using Deep Learning Convolution Neural Network and Harris Hawks Optimization

 Owing to the gravity of the diabetic disease the minimal level symptoms for diabetic failure in the early stage must be forecasted. The prediction system instantaneous and prior must thus be developed to eliminate serious medical factors. Information gathered from Pima Indian Diabetic dataset are synthesized through a profound learning approach that provides features for diabetic level information. Metadata is used to enhance the recognition process for the profound learned features. The distinct details retrieved by integrated machine and computer technology, including glucose level, health information, age, insulin level, etc. Due to the efficacious Hawks Optimization Algorithm (HOA), the data's insignificant participation in diabetic diagnostic processes is minimized in process analysis luminosity. Diabetic disease has been categorized with Deep Learning Convolution Networks (DLCNN) from among the chosen diabetic characteristics. The process output developed is measured on the basis of test results in terms of error rate, sensitivity, specificity and accuracy

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

New nature-inspired metaheuristics applied to the constrained optimization of a heavy-duty gas turbine operation

Orientador: Prof. Dr. Leandro dos Santos CoelhoTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 26/11/2020Inclui referências: p. 94-107Área de concentração: Sistemas EletrônicosResumo: Os codigos computacionais complexos das mais diversas areas, tais como industria 4.0 e energia, apresentam caracteristicas como nao-linearidade, escala, multimodalidade e presenca de restricoes. Por este motivo, as tecnicas classicas Newtonianas e baseadas em gradiente nao sao recomendadas para problemas de otimizacao global, os quais contem inumeras variaveis de projeto, restricoes e simulacoes incorporadas. Isso incentivou novas pesquisas em metaheuristicas baseadas em fenomenos naturais, principalmente comportamentos de animais com caracteristicas cooperativas ou colaborativas. Entretanto, nao existe um algoritmo unico capaz de ter bom desempenho para todos os tipos de problemas de otimizacao, o que justifica a busca recorrente por novas abordagens para solucionar esses problemas. Portanto, a presente tese introduz duas metaheuristicas com estruturas inovadoras inspiradas na natureza e nunca propostas. A primeira e baseada na especie Canis latrans e denominada Algoritmo de Otimizacao dos Coiotes (do ingles Coyote Optimization Algorithm, COA). A segunda, por sua vez, e inspirada na especie Cebus capucinus e denominada Otimizador dos Macacos-prego-da-cara-branca (do ingles Whitefaced Capuchin Monkeys Optimizer, WfCMO). Os algoritmos propostos sao avaliados sob um conjunto de funcoes de benchmarks empregadas nas competicoes do Congresso de Computacao Evolutiva (do ingles Congress on Evolutionary Computation, CEC) organizado pelo Instituto de Engenheiros Eletricistas e Eletronicos (do ingles Institute of Electrical and Electronics Engineers, IEEE) e comparadas a outras metaheuristicas inspiradas na natureza. Alem disso, a modelagem de um problema de otimizacao com restricoes de uma turbina a gas do tipo heavy-duty de uma termeletrica brasileira tambem e proposto nesta pesquisa. Para soluciona-lo, uma versao cultural do COA e proposta e seu desempenho e avaliado e comparado com outros algoritmos do estado-da-arte. Os resultados mostram que as metaheuristicas propostos nesta pesquisa alcancaram desempenho satisfatorio e superaram os outros algoritmos com 95% de confianca estatistica com base no teste nao-parametrico deWilcoxon-Mann-Whitney e tambem nos criterios do IEEE CEC 2017. Ainda, os resultados conquistados para problems multimodais e de alta dimensao mostram que as tecnicas sao promissoras para estes tipos de problema, que sao usuais em problemas reais. Ademais, as analises de curva de convergencia e de diversidade da populacao indicam um balanco adequado entre exploracao e aproveitamento. Por fim, a versao cultural do COA, que se demonstrou capaz de evitar convergencia prematura, superou os demais algoritmos do estado-da-arte para o problema de otimizacao da operacao da turbina. Palavras-chave: Industria 4.0, Inteligencia Computacional, Otimizacao Global, Metaheuristicas inspiradas na natureza.Abstract: The real-world applications from the most diverse fields such as industry 4.0 and energy have been formulated into complex computational codes with features as non-linearity, scale, multimodality, and the presence of constraints. Because of that, the classic Newtonians and gradient-based techniques are not recommended for global optimization applications with many design variables, constraints, and simulations embedded. It has encouraged new researches on metaheuristics based on natural phenomena, mainly animal behaviors with cooperative or collaborative features. However, there is not a unique algorithm able to perform well for all types of optimization problems, which justifies the recurrent search for new approaches. Hence, this thesis presents two never-proposed nature-inspired metaheuristics with innovative structures. The first one is based on the Canis latrans species and it is denoted Coyote Optimization Algorithm (COA). The second one is inspired by the Cebus capucinus species and receives the name of White-faced Capuchin Monkeys Optimizer (WfCMO). The proposed algorithms are evaluated under a set of benchmark functions employed in the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) competitions and compared to other state-of-the-art nature-inspired metaheuristics. Besides, the design of a constrained optimization problem of a heavy-duty gas turbine operation from a Brazilian thermoelectric power plant is proposed in this research. To solve it, a cultural version of the COA is proposed and its performance is evaluated and compared to other state-of-the-art algorithms. The results show that the proposed metaheuristics achieve profitable performance and outperform some state-of-the-art algorithms with 95% of statistical confidence based on the Wilcoxon-Mann- Whitney non-parametric test and the criteria of the IEEE CEC of 2017. Also, these algorithms present promising results for multimodal and high dimensional problems, which are the most usual features of real-world problems. Moreover, the convergence and diversity curves indicate a suitable balance between exploration and exploitation. Further, the proposed cultural version of the COA outperforms other state-of-the-art algorithms for the gas turbine operation problem. Its ability to avoid premature convergence is also demonstrated. Keywords: Industry 4.0, Computational Intelligence, Global Optimization, Nature-Inspired Metaheuristics

Development of Self-Learning Type-2 Fuzzy Systems for System Identification and Control of Autonomous Systems

Modelling and control of dynamic systems are faced by multiple technical challenges, mainly due to the nature of uncertain complex, nonlinear, and time-varying systems. Traditional modelling techniques require a complete understanding of system dynamics and obtaining comprehensive mathematical models is not always achievable due to limited knowledge of the systems as well as the presence of multiple uncertainties in the environment. As universal approximators, fuzzy logic systems (FLSs), neural networks (NNs) and neuro-fuzzy systems have proved to be successful computational tools for representing the behaviour of complex dynamical systems. Moreover, FLSs, NNs and learning-based techniques have been gaining popularity for controlling complex, ill-defined, nonlinear, and time-varying systems in the face of uncertainties. However, fuzzy rules derived by experts can be too ad-hoc, and the performance is less than optimum. In other words, generating fuzzy rules and membership functions in fuzzy systems is a potential challenge especially for systems with many variables. Moreover, under the umbrella of FLSs, although type-1 fuzzy logic control systems (T1-FLCs) have been applied to control various complex nonlinear systems, they have limited capability to handle uncertainties. Aiming to accommodate uncertainties, type-2 fuzzy logic control systems (T2-FLCs) were established. This thesis aims to address the shortcomings of existing fuzzy techniques by utilisation of type-2 FLCs with novel adaptive capabilities. The first contribution of this thesis is a novel online system identification technique by means of a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) to accommodate the footprint-of-uncertainties (FoUs). This development is meant to specifically address the shortcomings of type-1 fuzzy systems in capturing the footprint-of-uncertainties such as mechanical wear, rotor damage, battery drain and sensor and actuator faults. Unlike previous type-2 TS fuzzy models, the proposed method constructs two fuzzifiers (upper and lower) and two regression coefficients in the consequent part to handle uncertainties. The weighted least square method is employed to compute the regression coefficients. The proposed method is validated using two benchmarks, namely, real flight test data of a quadcopter drone and Mackey-Glass time series data. The algorithm has the capability to model uncertainties (e.g., noisy dataset). The second contribution of this thesis is the development of a novel self-adaptive interval type-2 fuzzy controller named the SAF2C for controlling multi-input multi-output (MIMO) nonlinear systems. The adaptation law is derived using sliding mode control (SMC) theory to reduce the computation time so that the learning process can be expedited by 80% compared to separate single-input single-output (SISO) controllers. The system employs the `Enhanced Iterative Algorithm with Stop Condition' (EIASC) type-reduction method, which is more computationally efficient than the `Karnik-Mendel' type-reduction algorithm. The stability of the SAF2C is proven using the Lyapunov technique. To ensure the applicability of the proposed control scheme, SAF2C is implemented to control several dynamical systems, including a simulated MIMO hexacopter unmanned aerial vehicle (UAV) in the face of external disturbance and parameter variations. The ability of SAF2C to filter the measurement noise is demonstrated, where significant improvement is obtained using the proposed controller in the face of measurement noise. Also, the proposed closed-loop control system is applied to control other benchmark dynamic systems (e.g., a simulated autonomous underwater vehicle and inverted pendulum on a cart system) demonstrating high accuracy and robustness to variations in system parameters and external disturbance. Another contribution of this thesis is a novel stand-alone enhanced self-adaptive interval type-2 fuzzy controller named the ESAF2C algorithm, whose type-2 fuzzy parameters are tuned online using the SMC theory. This way, we expect to design a computationally efficient adaptive Type-2 fuzzy system, suitable for real-time applications by introducing the EIASC type-reducer. The proposed technique is applied on a quadcopter UAV (QUAV), where extensive simulations and real-time flight tests for a hovering QUAV under wind disturbances are also conducted to validate the efficacy of the ESAF2C. Specifically, the control performance is investigated in the face of external wind gust disturbances, generated using an industrial fan. Stability analysis of the ESAF2C control system is investigated using the Lyapunov theory. Yet another contribution of this thesis is the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). T2-EFCS has two phases, namely, the structure learning and the parameters learning. The structure of T2-EFCS does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one rule. The rules are then added and pruned in an online fashion to achieve the desired set-point. The proposed technique is applied to control an unmanned ground vehicle (UGV) in the presence of multiple external disturbances demonstrating the robustness of the proposed control systems. The proposed approach turns out to be computationally efficient as the system employs fewer fuzzy parameters while maintaining superior control performance

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing