Intelligent nonsingular terminal sliding-mode control via perturbed fuzzy neural network

[[abstract]]In this paper, an intelligent nonsingular terminal sliding-mode control (INTSMC) system, which is composed of a terminal neural controller and a robust compensator, is proposed for an unknown nonlinear system. The terminal neural controller including a perturbed fuzzy neural network (PFNN) is the main controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the PFNN upon the system stability. The PFNN is used to approximate an unknown nonlinear term of the system dynamics and perturbed asymmetric membership functions are used to handle rule uncertainties when it is hard to exactly determine the grade of membership functions. In additional, Lyapunov stability theory is used to discuss the parameter learning and system stability of the INTSMC system. Finally, the proposed INTSMC system is applied to an inverted pendulum and a voice coil motor actuator. The simulation and experimental results show that the proposed INTSMC system can achieve favorable tracking performance and is robust against parameter variations in the plant

The 1991 3rd NASA Symposium on VLSI Design

Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2

Multiobjective programming for type-2 hierarchical fuzzy inference trees

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases. Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a different input’s combination, where the evolutionary process governs the input’s combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selectio

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Emotional Fuzzy Sliding-Mode Control for Unknown Nonlinear Systems

[[abstract]]The brain emotional learning model can be implemented with a simple hardware and processor; however, the learning model cannot model the qualitative aspects of human knowledge. To solve this problem, a fuzzy-based emotional learning model (FELM) with structure and parameter learning is proposed. The membership functions and fuzzy rules can be learned through the derived learning scheme. Further, an emotional fuzzy sliding-mode control (EFSMC) system, which does not need the plant model, is proposed for unknown nonlinear systems. The EFSMC system is applied to an inverted pendulum and a chaotic synchronization. The simulation results with the use of EFSMC system demonstrate the feasibility of FELM learning procedure. The main contributions of this paper are (1) the FELM varies its structure dynamically with a simple computation; (2) the parameter learning imitates the role of emotions in mammalians brain; (3) by combining the advantage of nonsingular terminal sliding-mode control, the EFSMC system provides very high precision and finite-time control performance; (4) the system analysis is given in the sense of the gradient descent method.[[notice]]補正完

New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

Premio Extraordinario de Doctorado de la UAH en 2013Lozano Rodríguez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study

New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

Premio Extraordinario de Doctorado de la UAH en 2013Lozano Rodríguez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI