This paper addresses the positioning-tracking control problem for a second-order direct-drive linear-switched reluctance machine (LSRM) motion control system based on the vectorization technique. In order to overcome ...
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This paper addresses the positioning-tracking control problem for a second-order direct-drive linear-switched reluctance machine (LSRM) motion control system based on the vectorization technique. In order to overcome the system-matrix dimension oversize problem caused by vectorizationmethod, the H-representation technique is adopted to reduce the closed-loop system-matrix dimension. The stability conditions with lower computational complexity for the LSRM motion control system are obtained based on Lyapunov stability theory and the vectorization technique. The positioning-tracking controller design method is proposed according to the matrix eigen-value numerical-analysis method. The proposed controller design method theoretically explicitly specifies the range of the designed controller gains, which can greatly reduce the burden of setting and tuning the control parameters as compared with proportion-integral-derivative parameters tuning method. Several groups of experimental tests are presented to verify the effectiveness of the proposed positioning-tracking control method for LSRM motion control systems.
To obtain the superiority property of solving time-varying linear matrix inequalities (LMIs), three novel finite-time convergence zeroing neural network (FTCZNN) models are designed and analyzed in this paper. First, ...
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To obtain the superiority property of solving time-varying linear matrix inequalities (LMIs), three novel finite-time convergence zeroing neural network (FTCZNN) models are designed and analyzed in this paper. First, to make the Matlab toolbox calculation processing more conveniently, the matrix vectorization technique is used to transform matrix-valued FTCZNN models into vector-valued FTCZNN models. Then, considering the importance of nonlinear activation functions on the conventional zeroing neural network (ZNN), the sign-bi-power activation function (AF), the improved sign-bi-power AF and the tunable signbi-power AF are explored to establish the FTCZNN models. Theoretical analysis shows that the FTCZNN models not only can accelerate the convergence speed, but also can achieve finite-time convergence. Computer numerical results ulteriorly confirm the effectiveness and advantages of the FTCZNN models for finding the solution set of time-varying LMIs. (C) 2020 Elsevier B.V. All rights reserved.
As an innovative design for high performance computing, Intel Xeon Phi coprocessor based on Intel Many Integrated Core (Intel MIC) architecture relies heavily on its SIMD (single instruction multiple data) unit. Howev...
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ISBN:
(纸本)9781479989379
As an innovative design for high performance computing, Intel Xeon Phi coprocessor based on Intel Many Integrated Core (Intel MIC) architecture relies heavily on its SIMD (single instruction multiple data) unit. However, performance of non-contiguous memory access has become the memory wall towards efficient utilization of SIMD unit on Intel Xeon Phi coprocessors due to gather/scatter overhead. Existing vectorization techniques in the optimization of gather/scatter overhead have been focusing on extracting data parallelism from inter-loop and intra-loop in a decoupled means. In this paper, we propose a novel inter-intra-hybrid vectorization technique which further exploits SIMD efficiency. In this technique, we generate optimized SIMD code for loops requesting non-contiguous memory. Additional strategies are also presented to improve SIMD unit parallelism through data padding and redundant computation. To evaluate our technique, the two major functions from Sandia's miniMD benchmark, i.e., LJ force calculation and neighbor list build, are taken for experiments which show that our proposed method achieves a performance gain of 25%-40% compared with Intel compiler auto vectorized code and outperforms the existing methods. Our optimization method can be further applied to other highly parallel workloads with frequent non-contiguous memory access, which is very common in real-world scientific applications.
In this paper, we propose an efficient Artificial Neural Network (ANN) based on the global search capacity of evolutionary algorithms (EAs) to identify damages in laminated composite structures. With remarkable advanc...
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In this paper, we propose an efficient Artificial Neural Network (ANN) based on the global search capacity of evolutionary algorithms (EAs) to identify damages in laminated composite structures. With remarkable advances, ANN has taken off over the last decades. However, ANN also has major drawbacks relating to local minima issues because it applies backpropagation algorithms based on gradient descent (GD) techniques. This leads to a substantial reduction in the effectiveness and accuracy of ANN. Some researchers have been come up with some solutions to tackle the local minimal problems of ANN by looking for starting beneficial points to eliminate initial local minima based on the global search capacity of stochastic algorithms. Nevertheless, it is commonly acknowledged that those solutions are no longer useful or even counterproductive in some cases if the network contains too many local minima distributed deeply in the search space. Hence, we propose a novel approach applying the fast convergence speed of GD techniques of ANN and the global search capacity of EAs to train the network. The core idea is that EAs are employed to work parallel with ANN during the process of training the network. This guarantees that the network possibly determines the best solution fast and avoids getting stuck in local minima. To enhance the efficiency of the global search capacity, in this work, a hybrid metaheuristic optimization algorithm (HGACS) of EAs is also proposed, which possibly gains the advantages of both Genetic Algorithm (GA) and Cuckoo Search (CS). GA is applied to generate initial populations with the best quality derived from the ability of crossover and mutation operators, whereas CS with global search capacity is used to seek the best solution. Moreover, to deal with the large amount of data utilized to train the network, a vectorization technique is applied for the data of the objective function, which considerably decreases the computational cost. The obtained res
As an innovative design for high performance computing, Intel Xeon Phi coprocessor based on Intel Many Integrated Core (Intel MIC) architecture relies heavily on its SIMD (single instruction multiple data) unit. Howev...
详细信息
ISBN:
(纸本)9781479989386
As an innovative design for high performance computing, Intel Xeon Phi coprocessor based on Intel Many Integrated Core (Intel MIC) architecture relies heavily on its SIMD (single instruction multiple data) unit. However, performance of non-contiguous memory access has become the memory wall towards efficient utilization of SIMD unit on Intel Xeon Phi coprocessors due to gather/scatter overhead. Existing vectorization techniques in the optimization of gather/scatter overhead have been focusing on extracting data parallelism from inter-loop and intra-loop in a decoupled means. In this paper, we propose a novel inter-intra-hybrid vectorization technique which further exploits SIMD efficiency. In this technique, we generate optimized SIMD code for loops requesting non-contiguous memory. Additional strategies are also presented to improve SIMD unit parallelism through data padding and redundant computation. To evaluate our technique, the two major functions from Sandia's miniMD benchmark, i.e., LJ force calculation and neighbor list build, are taken for experiments which show that our proposed method achieves a performance gain of 25%-40% compared with Intel compiler auto vectorized code and outperforms the existing methods. Our optimization method can be further applied to other highly parallel workloads with frequent non-contiguous memory access, which is very common in real-world scientific applications.
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