A Particle-Swarm-Optimized Fuzzy–Neural Network for Voice-Controlled Robot Systems

Chatterjee, Amitava; Pulasinghe, Koliya; Watanabe, Keigo; Izumi, Kiyotaka

doi:10.1109/tie.2005.858737

Cited by 187 publications

(64 citation statements)

References 16 publications

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“…PSO has been shown to be very effective in optimizing challenging multidimensional, nonlinear and multimodal problems in a variety of fields such as signal processing [20][21][22][23], communication networks [24], biomedical [25,26], control [27,28], robotics [29], power systems [30], electromagnetics [31], image and video analysis [32,33]. It was inspired by the social behavior of animals, specifically the ability of groups of animals to work collectively in finding the desirable positions in a given area.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multipath channel identification by using global optimization in ambiguity function domain

Güldoǧan

Arıkan

2011

Signal Processing

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a b s t r a c tA new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multipath channel identification by using global optimization in ambiguity function domain

Güldoǧan

Arıkan

2011

Signal Processing

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“…Medicherla 17 describes the implementation of a voice-controllable intelligent user interface for a Pioneer 3-AT mobile robot. However, both works 16,17 do not show results about their recognition rates. The integration of voice control into a HRI would make humans feel more comfortable during the interaction.…”

mentioning

confidence: 93%

“…Finally, it must be pointed out that, as in this paper, examples of teleoperation of a robotic platform can also be reported. 16, 17 Chatterjee 16 uses a fuzzy-neural network as a building block in a robot system, controlled by voice-based commands. Medicherla 17 describes the implementation of a voice-controllable intelligent user interface for a Pioneer 3-AT mobile robot.…”

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confidence: 99%

Command-based voice teleoperation of a mobile robot via a human-robot interface

Poncela

Gallardo-Estrella

2014

Robotica

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SUMMARYVerbal communication is the most natural way of human-robot interaction. Such an interaction is usually achieved by means of a human-robot interface (HRI). In this paper, a HRI is presented to teleoperate a robotic platform via the user's voice. Hence, a speech recognition system is necessary. In this work, a user-dependent acoustic model for Spanish speakers has been developed to teleoperate a robot with a set of commands. Experimental results have been successful, both in terms of a high recognition rate and the navigation of the robot under the control of the user's voice.

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“…The mentioned method is based on Particle Swarm Theory which is Section 3. Particle Swarm optimization (PSO), has recently attracted by many researches, since it's applicable in various problems such as classification [22], fuzzy modeling [21], fuzzy control [23], power system optimization [24] and etc. PSO algorithm which is constructed from bird flocking and fish schooling, is based on creating several candidate solutions (particles) for the multidimensional optimization function.…”

Section: Introductionmentioning

confidence: 99%

Numerical solution of Sylvester matrix equations: Application to dynamical systems

Asari¹,

Amirfakhrian²

2016

Journal of Interpolation and Approximation in Scientific Comput

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Many problems of control theory specially dynamical system lead to Sylvester equations. In this paper, we employ an iterative method of optimization based on partial swarm theory to solve the Sylvester system. To this purpose we consider dynamical system with different construction of state observer which lead to Sylvester observer equation. Using Pso to optimize the solution, obtain the solution with high accuracy comparison with other numerical methods, since the stability analysis of particle dynamics of PSO associated with the best particle is based on nonlinear feedback systems. Finally, some examples demonstrate the efficiency of the proposed method.

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A Particle-Swarm-Optimized Fuzzy–Neural Network for Voice-Controlled Robot Systems

Cited by 187 publications

References 16 publications

Multipath channel identification by using global optimization in ambiguity function domain

Multipath channel identification by using global optimization in ambiguity function domain

Command-based voice teleoperation of a mobile robot via a human-robot interface

Numerical solution of Sylvester matrix equations: Application to dynamical systems

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