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第八届中国多智能体系统与控制会议(MASC'2012)

作者： Yongkui Liu Lin Zhang Xiaojie Chen Long Wang School of Automation Science and Electrical Engineering Beihang University - Beijing 100191China Sc School of Automation Science and Electrical Engineering Beihang University - Beijing 100191China Evolution and Ecology Program International Institute for Applied Systems Analysis(IIASA)- Schlosspl Center for Systems and Control State Key Laboratory for Turbulence and Complex SystemsCollege of En

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Accelerating the Artificial Bee Colony Algorithm by Hardware Parallel Implementations

Accelerating the Artificial Bee Colony Algorithm by Hardware...

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IEEE Latin American Symposium on Circuits and systems

作者： Daniel M. Mufioz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincon Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Department of Computer Science and Mathematics University of Brasilia

ISBN: (纸本)9781467312073

Artificial bee colony (ABC) is an optimization algorithm inspired on the intelligent behavior of honey bee swarms. It is suitable to be applied when mathematical techniques are impractical or provide suboptimal solutions. As a population-based algorithm, the ABC suffers on large execution times specifically for embedded optimization problems with computational limitations. For that we propose a hardware parallel architecture of the opposition-based ABC algorithm (HPOABC) that facilitates the implementation in Field programmable Gate Arrays (FPGAs). Numerical simulations using four well-known benchmark problems demonstrate that the opposition-based approach allows the algorithm to improve its functionality, preserving the swarm diversity. Additionally, synthesis results point outs that the HPOABC architecture is effectively mapped in hardware and is suitable for embedded applications.

关键词： Global optimization Swarm intelligence Artificial bee colony FPGAs Floating-point arithmetic

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A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

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Advances in Artificial Neural systems 2012年第1期2012卷

作者： X. Zhang G. Foderaro C. Henriquez A. M. J. VanDongen S. Ferrari Laboratory for Intelligent Systems and Control (LISC) Department of Mechanical Engineering and Materials Science Duke University Durham NC 27708 USAduke.edu Department of Biomedical Engineering and Department of Computer Science Duke University Durham NC 27708 USAduke.edu Program in Neuroscience & Behavioral Disorders Duke-NUS Graduate Medical School Singapore Singapore

This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network.

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Hardware Particle Swarm Optimization with passive congregation for embedded applications

Hardware Particle Swarm Optimization with passive congregati...

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Southern Conference on programmable Logic

作者： Muñoz, Daniel M. Llanos, Carlos H. Coelho, Leandro Dos S. Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424488483

Achieving high performance optimization algorithms for embedded applications can be very challenging, particularly when several requirements such as high accuracy computations, short elapsed time, area cost, low power consumption and portability must be accomplished. This paper proposes a hardware implementation of the Particle Swarm Optimization algorithm with passive congregation (HPPSOpc), which was developed using several floating-point arithmetic libraries. The passive congregation is a biological behavior which allows the swarm to preserve its integrity, balancing between global and local search. The HPPSOpc architecture was implemented on a Virtex5 FPGA device and validated using two multimodal benchmark problems. Synthesis, simulation and execution time results demonstrates that the passive congregation approach is a low cost solution for solving embedded optimization problems with a high performance. © 2011 IEEE.

关键词： Digital arithmetic

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Opposition-based shuffled PSO with passive congregation applied to FM matching synthesis

Opposition-based shuffled PSO with passive congregation appl...

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IEEE Congress on Evolutionary Computation

作者： Munoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincon, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424478347

Synthesis of musical instruments or human voice is a time consuming process which requires theoretical and experimental knowledge about the synthesis engine. Commonly, performers need to deal with synthesizer interfaces and a process of trial and error for creating musical sounds similar to a target sound. This drawback can be overcome by adjusting automatically the synthesizer parameters using optimization algorithms. In this paper a hybrid particle swarm optimization (PSO) algorithm is proposed to solve the frequency modulation (FM) matching synthesis problem. The proposed algorithm takes advantage of a shuffle process for exchanging information between particles and applies the selective passive congregation and the opposition-based learning approaches to preserve swarm diversity. Both approaches for injecting diversity are based on simple operators, preserving the easy implementation philosophy of the particle swarm optimization. The proposed hybrid particle swarm optimization algorithm was validated for a three-nested FM synthesizer, which represents a 6-dimensional multimodal optimization problem with strong epistasis. Simulation results revealed that the proposed algorithm presented promising results in terms of quality of solutions. © 2011 IEEE.

关键词： Global optimization

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Comparison of disturbance rejection performance between sliding-mode control and equivalent-input-disturbance approach

Comparison of disturbance rejection performance between slid...

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IEEE International Conference on Mechatronics

作者： She, Jinhua Pan, Yaodong Hashimoto, Hiroshi Wu, Min School of Computer Science Tokyo Univ. of Tech. Hachioji 192-0982 Japan Control Systems Eng Honeywell Canada Mississauga ON L5L3S6 Canada Master Program of Innovation for Design and Eng Advanced Institute of Industrial Tech. Tokyo 140-0011 Japan School of Information Science and Engineering Central South Univ. Changsha 410083 China

ISBN: (纸本)9781612849836

Sliding mode control (SMC) has been considered as a powerful method for disturbances rejection. On the other hand, we have presented an equivalent-input disturbance (EID) approach to reject disturbances. This paper takes a dual-stage feed drive as an example to compare these two methods. The example shows that, while the EID approach obtains almost the same disturbance rejection performance for matched disturbances as that of the SMC does, it can reject unmatched disturbances. This shows the superiority of the EID approach over SMC method. © 2011 IEEE.

关键词： Sliding mode control

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Hardware Particle Swarm Optimization with passive congregation for embedded applications

Hardware Particle Swarm Optimization with passive congregati...

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Southern Conference on programmable Logic

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brazil

关键词： Hardware Computer architecture Software Field programmable gate arrays Benchmark testing Optimization Software algorithms

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Opposition-based shuffled PSO with passive congregation applied to FM matching synthesis

Opposition-based shuffled PSO with passive congregation appl...

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Congress on Evolutionary Computation

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia Brazil

关键词： Frequency modulation Optimization Equations Convergence Particle swarm optimization Genetic algorithms

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Comparison of disturbance rejection performance between sliding-mode control and equivalent-input-disturbance approach

Comparison of disturbance rejection performance between slid...

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International Conference on Mechatronics (ICM)

作者： Jinhua She Yaodong Pan Hiroshi Hashimoto Min Wu School of Computer Science Tokyo University of Technology Hachioji Tokyo Japan Control Systems Eng Honeywell Canada Mississauga ONT Canada Master Program of Innovation for Design and Eng Advanced Institute of Industrial Technology Tokyo Japan School of Information Science and Engineering Central South University Changsha China

关键词： Equations Observers Manganese Frequency control

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Hardware particle swarm optimization based on the attractive-repulsive scheme for embedded applications

Hardware particle swarm optimization based on the attractive...

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2010 International Conference on Reconfigurable Computing and FPGAs, ReConFig 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9780769543147

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. However, the PSO algorithm suffers from premature convergence and high elapsed time when solving multimodal and large scale engineering problems. This problem becomes an evident drawback for embedded applications in which the microcontrollers often operates at low computational capacity. This paper proposes a hardware implementation of a parallel self-adaptive PSO algorithm based on an attractive-repulsive scheme and using the efficient floating-point arithmetic which performs computations with large dynamic range and high precision. The parallel capabilities of the PSO are exploited by implementing parallel particles directly in hardware in order to decrease the running time. In addition, the attractive-repulsive technique avoids the premature convergence problem by self-adapting the swarm behavior according to its diversity. Synthesis and simulation results for benchmark test problems were performed, demonstrating the correctness of the proposed architectures. Finally, an elapsed time comparison between the hardware and software implementations shows the suitableness of the proposed architecture for embedded applications. © 2010 IEEE.

关键词： Global optimization

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