Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, ...Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, a novel competition-based coordination model is proposed to solve the multi-robot task allocation problem and applied to a multi-robot object tracking scenario.展开更多
Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts itera...Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.展开更多
Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)network.After obtaining the network,theorems and related proofs are provided to guarantee the exponential ...Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)network.After obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA network.Then,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ones.Finally,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.展开更多
Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeas...Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeasibility in finite-time convergence based on the Lipschitz continuity.展开更多
基金supported by the Key Cooperation Project of Chongqing Municipal Education Commission (HZ2021008, HZ2021017)the Project of “Fertilizer Robot” of Chongqing Committee on Agriculture and Rural Affairs。
文摘Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, a novel competition-based coordination model is proposed to solve the multi-robot task allocation problem and applied to a multi-robot object tracking scenario.
基金supported in part by the National Natural Science Foundation of China (6177249391646114)+1 种基金Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020)in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences
文摘Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.
基金supported in part by the National Natural Science Foundation of China(62176109)the Natural Science Foundation of Gansu Province(21JR7RA531)+6 种基金the Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province(2021-Z-003)the CAS“Light of West China”Programthe Natural Science Foundation of Chongqing(China)(cstc2020jcyjzdxm X0028)the Chongqing Entrepreneurship and Innovation Support Program for Overseas Returnees(CX2021100)the Supercomputing Center of Lanzhou Universitythe Science and Technology Project of Chengguan District of Lanzhou(2021JSCX0014)the Education Department of Gansu Province:Excellent Graduate Student“Innovation Star”Project(2021CXZX-122)。
文摘Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)network.After obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA network.Then,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ones.Finally,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.
基金supported by the National Natural Science Foundation of China(62072429)the Key Cooperation Project of Chongqing Municipal Education Commission(HZ2021017,HZ2021008)。
文摘Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeasibility in finite-time convergence based on the Lipschitz continuity.
