Velocity control of longitudinal vibration ultrasonic motor using improved Elman neural network trained by CQPSO with Lévy flights

Longitudinally vibration ultrasonic motor (LV-USM), a canonical nonlinear system, utilizes the inverse piezoelectric effect of piezoelectric ceramic to generate the mechanical vibration within the scope of ultrasonic frequency. However, it is very difficult to establish a strict and accurate mathematical model. Hence seeking a dynamic identifier and controller for LV-USM avoiding the accurate mathematical model becomes a feasible approach. In this paper, a novel learning algorithm for dynamic recurrent Elman neural networks is present based on a particle swarm optimization (PSO) to identify and control an LV-USM. To overcome the PSO’s global search ability, Lévy flights, a kind of random walks, are imported to improve the ability of exploration rather than Brownian motion or Gauss disturbance based on Cooperative Quantum-behaved PSO (CQPSO). Thereafter, a controller is designed to perform speed control for LV-USM along with the nonlinear identification also using this kind of neural network. By discrete Lyapunov stability approach, the controller is proven to be stable theoretically and the latter trial shows its robustness of anti-noise performance. In the experiments, the numerical results illustrate that the designed identifier and controller can achieve both higher convergence precision and speed, relative to current state-of-the-art other methods. Moreover, this controller shows lower control error than other approaches while the displacement of the rotor disc in LV-USM appears more smooth and uniform

Velocity control of longitudinal vibration ultrasonic motor using improved Elman neural network trained by CQPSO with Lévy flights

Longitudinally vibration ultrasonic motor (LV-USM), a canonical nonlinear system, utilizes the inverse piezoelectric effect of piezoelectric ceramic to generate the mechanical vibration within the scope of ultrasonic frequency. However, it is very difficult to establish a strict and accurate mathematical model. Hence seeking a dynamic identifier and controller for LV-USM avoiding the accurate mathematical model becomes a feasible approach. In this paper, a novel learning algorithm for dynamic recurrent Elman neural networks is present based on a particle swarm optimization (PSO) to identify and control an LV-USM. To overcome the PSO’s global search ability, Lévy flights, a kind of random walks, are imported to improve the ability of exploration rather than Brownian motion or Gauss disturbance based on Cooperative Quantum-behaved PSO (CQPSO). Thereafter, a controller is designed to perform speed control for LV-USM along with the nonlinear identification also using this kind of neural network. By discrete Lyapunov stability approach, the controller is proven to be stable theoretically and the latter trial shows its robustness of anti-noise performance. In the experiments, the numerical results illustrate that the designed identifier and controller can achieve both higher convergence precision and speed, relative to current state-of-the-art other methods. Moreover, this controller shows lower control error than other approaches while the displacement of the rotor disc in LV-USM appears more smooth and uniform

On Applications of New Soft and Evolutionary Computing Techniques to Direct and Inverse Modeling Problems

Adaptive direct modeling or system identification and adaptive inverse modeling or channel equalization find extensive applications in telecommunication, control system, instrumentation, power system engineering and geophysics. If the plants or systems are nonlinear, dynamic, Hammerstein and multiple-input and multiple-output (MIMO) types, the identification task becomes very difficult. Further, the existing conventional methods like the least mean square (LMS) and recursive least square (RLS) algorithms do not provide satisfactory training to develop accurate direct and inverse models. Very often these (LMS and RLS) derivative based algorithms do not lead to optimal solutions in pole-zero and Hammerstein type system identification problem as they have tendency to be trapped by local minima. In many practical situations the output data are contaminated with impulsive type outliers in addition to measurement noise. The density of the outliers may be up to 50%, which means that about 50% of the available data are affected by outliers. The strength of these outliers may be two to five times the maximum amplitude of the signal. Under such adverse conditions the available learning algorithms are not effective in imparting satisfactory training to update the weights of the adaptive models. As a result the resultant direct and inverse models become inaccurate and improper. Hence there are three important issues which need attention to be resolved. These are : (i) Development of accurate direct and inverse models of complex plants using some novel architecture and new learning techniques. (ii) Development of new training rules which alleviates local minima problem during training and thus help in generating improved adaptive models. (iii) Development of robust training strategy which is less sensitive to outliers in training and thus to create identification and equalization models which are robust against outliers. These issues are addressed in this thesis and corresponding contribution are outlined in seven Chapters. In addition, one Chapter on introduction, another on required architectures and algorithms and last Chapter on conclusion and scope for further research work are embodied in the thesis. A new cascaded low complexity functional link artificial neural network (FLANN) structure is proposed and the corresponding learning algorithm is derived and used to identify nonlinear dynamic plants. In terms of identification performance this model is shown to outperform the multilayer perceptron and FLANN model. A novel method of identification of IIR plants is proposed using comprehensive learning particle swarm optimization (CLPSO) algorithm. It is shown that the new approach is more accurate in identification and takes less CPU time compared to those obtained by existing recursive LMS (RLMS), genetic algorithm (GA) and PSO based approaches. The bacterial foraging optimization (BFO) and PSO are used to develop efficient learning algorithms to train models to identify nonlinear dynamic and MIMO plants. The new scheme takes less computational effort, more accurate and consumes less input samples for training. Robust identification and equalization of complex plants have been carried out using outliers in training sets through minimization of robust norms using PSO and BFO based methods. This method yields robust performance both in equalization and identification tasks. Identification of Hammerstein plants has been achieved successfully using PSO, new clonal PSO (CPSO) and immunized PSO (IPSO) algorithms. Finally the thesis proposes a distributed approach to identification of plants by developing two distributed learning algorithms : incremental PSO and diffusion PSO. It is shown that the new approach is more efficient in terms of accuracy and training time compared to centralized PSO based approach. In addition a robust distributed approach for identification is proposed and its performance has been evaluated. In essence the thesis proposed many new and efficient algorithms and structure for identification and equalization task such as distributed algorithms, robust algorithms, algorithms for ploe-zero identification and Hammerstein models. All these new methods are shown to be better in terms of performance, speed of computation or accuracy of results

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

A New Mutated Quantum-Behaved Particle Swarm Optimizer for Digital IIR Filter Design

Adaptive infinite impulse response (IIR) filters have shown their worth in a wide range of practical applications. Because the error surface of IIR filters is multimodal in most cases, global optimization techniques are required for avoiding local minima. In this paper, we employ a global optimization algorithm, Quantum-behaved particle swarm optimization (QPSO) that was proposed by us previously, and its mutated version in the design of digital IIR filter. The mechanism in QPSO is based on the quantum behaviour of particles in a potential well and particle swarm optimization (PSO) algorithm. QPSO is characterized by fast convergence, good search ability, and easy implementation. The mutated QPSO (MuQPSO) is proposed in this paper by using a random vector in QPSO to increase the randomness and to enhance the global search ability. Experimental results on three examples show that QPSO and MuQPSO are superior to genetic algorithm (GA), differential evolution (DE) algorithm, and PSO algorithm in quality, convergence speed, and robustness

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Antecipação na tomada de decisão com múltiplos critérios sob incerteza

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treinoAbstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolumeDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric