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Formalization of Generalized Constraint Language: A Crucial Prelude to Computing With Words.

IEEE transactions on cybernetics

Khorasani ES, Rahimi S, Calvert W.
PMID: 22801519
IEEE Trans Cybern. 2013 Feb;43(1):246-58. doi: 10.1109/TSMCB.2012.2204743. Epub 2012 Jul 11.

The generalized constraint language (GCL), introduced by Zadeh, serves as a basis for computing with words (CW). It provides an agenda to express the imprecise and fuzzy information embedded in natural language and allows reasoning with perceptions. Despite its...

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Khorasani ES, Rahimi S, Calvert W. Formalization of Generalized Constraint Language: A Crucial Prelude to Computing With Words. IEEE Trans Cybern. 2012;43(1):246-58doi: 10.1109/TSMCB.2012.2204743.
Khorasani, E. S., Rahimi, S., & Calvert, W. (2013). Formalization of Generalized Constraint Language: A Crucial Prelude to Computing With Words. IEEE transactions on cybernetics, 43(1), 246-58. https://doi.org/10.1109/TSMCB.2012.2204743
Khorasani, Elham S, et al. "Formalization of Generalized Constraint Language: A Crucial Prelude to Computing With Words." IEEE transactions on cybernetics vol. 43,1 (2013): 246-58. doi: https://doi.org/10.1109/TSMCB.2012.2204743
Khorasani ES, Rahimi S, Calvert W. Formalization of Generalized Constraint Language: A Crucial Prelude to Computing With Words. IEEE Trans Cybern. 2013 Feb;43(1):246-58. doi: 10.1109/TSMCB.2012.2204743. Epub 2012 Jul 11. PMID: 22801519.
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Data Partition Learning With Multiple Extreme Learning Machines.

IEEE transactions on cybernetics

Yang Y, Wu QM, Wang Y, Zeeshan KM, Lin X, Yuan X.
PMID: 25216495
IEEE Trans Cybern. 2015 Aug;45(8):1463-75. doi: 10.1109/TCYB.2014.2352594. Epub 2014 Sep 08.

As demonstrated earlier, the learning accuracy of the single-layer-feedforward-network (SLFN) is generally far lower than expected, which has been a major bottleneck for many applications. In fact, for some large real problems, it is accepted that after tremendous learning...

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Yang Y, Wu QM, Wang Y, et al. Data Partition Learning With Multiple Extreme Learning Machines. IEEE Trans Cybern. 2014;45(8):1463-75doi: 10.1109/TCYB.2014.2352594.
Yang, Y., Wu, Q. M., Wang, Y., Zeeshan, K. M., Lin, X., & Yuan, X. (2015). Data Partition Learning With Multiple Extreme Learning Machines. IEEE transactions on cybernetics, 45(8), 1463-75. https://doi.org/10.1109/TCYB.2014.2352594
Yang, Yimin, et al. "Data Partition Learning With Multiple Extreme Learning Machines." IEEE transactions on cybernetics vol. 45,8 (2015): 1463-75. doi: https://doi.org/10.1109/TCYB.2014.2352594
Yang Y, Wu QM, Wang Y, Zeeshan KM, Lin X, Yuan X. Data Partition Learning With Multiple Extreme Learning Machines. IEEE Trans Cybern. 2015 Aug;45(8):1463-75. doi: 10.1109/TCYB.2014.2352594. Epub 2014 Sep 08. PMID: 25216495.
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How Much Control is Enough for Network Connectivity Preservation and Collision Avoidance?.

IEEE transactions on cybernetics

Chen Z, Fan MC, Zhang HT.
PMID: 25291811
IEEE Trans Cybern. 2015 Aug;45(8):1647-56. doi: 10.1109/TCYB.2014.2357838. Epub 2014 Sep 29.

For a multiagent system in free space, the agents are required to generate sufficiently large cohesive force for network connectivity preservation and sufficiently large repulsive force for collision avoidance. This paper gives an energy function based approach for estimating...

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Chen Z, Fan MC, Zhang HT. How Much Control is Enough for Network Connectivity Preservation and Collision Avoidance?. IEEE Trans Cybern. 2014;45(8):1647-56doi: 10.1109/TCYB.2014.2357838.
Chen, Z., Fan, M. C., & Zhang, H. T. (2015). How Much Control is Enough for Network Connectivity Preservation and Collision Avoidance?. IEEE transactions on cybernetics, 45(8), 1647-56. https://doi.org/10.1109/TCYB.2014.2357838
Chen, Zhiyong, et al. "How Much Control is Enough for Network Connectivity Preservation and Collision Avoidance?." IEEE transactions on cybernetics vol. 45,8 (2015): 1647-56. doi: https://doi.org/10.1109/TCYB.2014.2357838
Chen Z, Fan MC, Zhang HT. How Much Control is Enough for Network Connectivity Preservation and Collision Avoidance?. IEEE Trans Cybern. 2015 Aug;45(8):1647-56. doi: 10.1109/TCYB.2014.2357838. Epub 2014 Sep 29. PMID: 25291811.
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An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention.

IEEE transactions on cybernetics

Shao Z, Er MJ, Wang N.
PMID: 26259254
IEEE Trans Cybern. 2016 Aug;46(8):1939-51. doi: 10.1109/TCYB.2015.2458177. Epub 2015 Jul 30.

It is well known that the architecture of the extreme learning machine (ELM) significantly affects its performance and how to determine a suitable set of hidden neurons is recognized as a key issue to some extent. The leave-one-out cross-validation...

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Shao Z, Er MJ, Wang N. An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention. IEEE Trans Cybern. 2015;46(8):1939-51doi: 10.1109/TCYB.2015.2458177.
Shao, Z., Er, M. J., & Wang, N. (2016). An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention. IEEE transactions on cybernetics, 46(8), 1939-51. https://doi.org/10.1109/TCYB.2015.2458177
Shao, Zhifei, et al. "An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention." IEEE transactions on cybernetics vol. 46,8 (2016): 1939-51. doi: https://doi.org/10.1109/TCYB.2015.2458177
Shao Z, Er MJ, Wang N. An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention. IEEE Trans Cybern. 2016 Aug;46(8):1939-51. doi: 10.1109/TCYB.2015.2458177. Epub 2015 Jul 30. PMID: 26259254.
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The generalization performance of regularized regression algorithms based on Markov sampling.

IEEE transactions on cybernetics

Zou B, Tang YY, Xu Z, Li L, Xu J, Lu Y.
PMID: 24184794
IEEE Trans Cybern. 2014 Sep;44(9):1497-507. doi: 10.1109/TCYB.2013.2287191. Epub 2013 Oct 30.

This paper considers the generalization ability of two regularized regression algorithms [least square regularized regression (LSRR) and support vector machine regression (SVMR)] based on non-independent and identically distributed (non-i.i.d.) samples. Different from the previously known works for non-i.i.d. samples,...

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Zou B, Tang YY, Xu Z, et al. The generalization performance of regularized regression algorithms based on Markov sampling. IEEE Trans Cybern. 2013;44(9):1497-507doi: 10.1109/TCYB.2013.2287191.
Zou, B., Tang, Y. Y., Xu, Z., Li, L., Xu, J., & Lu, Y. (2014). The generalization performance of regularized regression algorithms based on Markov sampling. IEEE transactions on cybernetics, 44(9), 1497-507. https://doi.org/10.1109/TCYB.2013.2287191
Zou, Bin, et al. "The generalization performance of regularized regression algorithms based on Markov sampling." IEEE transactions on cybernetics vol. 44,9 (2014): 1497-507. doi: https://doi.org/10.1109/TCYB.2013.2287191
Zou B, Tang YY, Xu Z, Li L, Xu J, Lu Y. The generalization performance of regularized regression algorithms based on Markov sampling. IEEE Trans Cybern. 2014 Sep;44(9):1497-507. doi: 10.1109/TCYB.2013.2287191. Epub 2013 Oct 30. PMID: 24184794.
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A competitive swarm optimizer for large scale optimization.

IEEE transactions on cybernetics

Cheng R, Jin Y.
PMID: 24860047
IEEE Trans Cybern. 2015 Feb;45(2):191-204. doi: 10.1109/TCYB.2014.2322602. Epub 2014 May 20.

In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position...

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Cheng R, Jin Y. A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern. 2014;45(2):191-204doi: 10.1109/TCYB.2014.2322602.
Cheng, R., & Jin, Y. (2015). A competitive swarm optimizer for large scale optimization. IEEE transactions on cybernetics, 45(2), 191-204. https://doi.org/10.1109/TCYB.2014.2322602
Cheng, Ran, and Jin, Yaochu. "A competitive swarm optimizer for large scale optimization." IEEE transactions on cybernetics vol. 45,2 (2015): 191-204. doi: https://doi.org/10.1109/TCYB.2014.2322602
Cheng R, Jin Y. A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern. 2015 Feb;45(2):191-204. doi: 10.1109/TCYB.2014.2322602. Epub 2014 May 20. PMID: 24860047.
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Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form.

IEEE transactions on cybernetics

Li DJ, Li DP.
PMID: 28920913
IEEE Trans Cybern. 2018 Sep;48(9):2633-2642. doi: 10.1109/TCYB.2017.2747628. Epub 2017 Sep 14.

In this paper, an adaptive output feedback control is framed for uncertain nonlinear discrete-time systems. The considered systems are a class of multi-input multioutput nonaffine nonlinear systems, and they are in the nested lower triangular form. Furthermore, the unknown...

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Li DJ, Li DP. Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form. IEEE Trans Cybern. 2017;48(9):2633-2642doi: 10.1109/TCYB.2017.2747628.
Li, D. J., & Li, D. P. (2018). Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form. IEEE transactions on cybernetics, 48(9), 2633-2642. https://doi.org/10.1109/TCYB.2017.2747628
Li, Dong-Juan, and Li, Da-Peng. "Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form." IEEE transactions on cybernetics vol. 48,9 (2018): 2633-2642. doi: https://doi.org/10.1109/TCYB.2017.2747628
Li DJ, Li DP. Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form. IEEE Trans Cybern. 2018 Sep;48(9):2633-2642. doi: 10.1109/TCYB.2017.2747628. Epub 2017 Sep 14. PMID: 28920913.
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Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays.

IEEE transactions on cybernetics

Park M, Lee SH, Kwon OM, Seuret A.
PMID: 28885163
IEEE Trans Cybern. 2018 Jul;48(7):2192-2202. doi: 10.1109/TCYB.2017.2729164. Epub 2017 Sep 06.

This paper investigates synchronization in complex dynamical networks (CDNs) with interval time-varying delays. The CDNs are representative of systems composed of a large number of interconnected dynamical units, and for the purpose of the mathematical analysis, the leading work...

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Park M, Lee SH, Kwon OM, et al. Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays. IEEE Trans Cybern. 2017;48(7):2192-2202doi: 10.1109/TCYB.2017.2729164.
Park, M., Lee, S. H., Kwon, O. M., & Seuret, A. (2018). Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays. IEEE transactions on cybernetics, 48(7), 2192-2202. https://doi.org/10.1109/TCYB.2017.2729164
Park, Myeongjin, et al. "Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays." IEEE transactions on cybernetics vol. 48,7 (2018): 2192-2202. doi: https://doi.org/10.1109/TCYB.2017.2729164
Park M, Lee SH, Kwon OM, Seuret A. Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays. IEEE Trans Cybern. 2018 Jul;48(7):2192-2202. doi: 10.1109/TCYB.2017.2729164. Epub 2017 Sep 06. PMID: 28885163.
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Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage.

IEEE transactions on cybernetics

Song H, Zhang WA, Yu L, Chen B.
PMID: 27705872
IEEE Trans Cybern. 2017 Dec;47(12):4367-4379. doi: 10.1109/TCYB.2016.2609503. Epub 2016 Sep 29.

In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other....

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Song H, Zhang WA, Yu L, et al. Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage. IEEE Trans Cybern. 2016;47(12):4367-4379doi: 10.1109/TCYB.2016.2609503.
Song, H., Zhang, W. A., Yu, L., & Chen, B. (2017). Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage. IEEE transactions on cybernetics, 47(12), 4367-4379. https://doi.org/10.1109/TCYB.2016.2609503
Song, Haiyu, et al. "Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage." IEEE transactions on cybernetics vol. 47,12 (2017): 4367-4379. doi: https://doi.org/10.1109/TCYB.2016.2609503
Song H, Zhang WA, Yu L, Chen B. Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage. IEEE Trans Cybern. 2017 Dec;47(12):4367-4379. doi: 10.1109/TCYB.2016.2609503. Epub 2016 Sep 29. PMID: 27705872.
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Delay-Dependent Algebraic Riccati Equation to Stabilization of Networked Control Systems: Continuous-Time Case.

IEEE transactions on cybernetics

Cheng Tan, Huanshui Zhang, Wing Shing Wong.
PMID: 28945606
IEEE Trans Cybern. 2018 Oct;48(10):2783-2794. doi: 10.1109/TCYB.2017.2750221. Epub 2017 Sep 19.

In this paper, a delay-dependent algebraic Riccati equation (DARE) approach is developed to study the meansquare stabilization problem for continuous-time networked control systems. Different from most previous studies that information transmission can be performed with zero delay and infinite...

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Cheng Tan, Huanshui Zhang, Wing Shing Wong. Delay-Dependent Algebraic Riccati Equation to Stabilization of Networked Control Systems: Continuous-Time Case. IEEE Trans Cybern. 2017;48(10):2783-2794doi: 10.1109/TCYB.2017.2750221.
Cheng Tan, Huanshui Zhang, & Wing Shing Wong. (2018). Delay-Dependent Algebraic Riccati Equation to Stabilization of Networked Control Systems: Continuous-Time Case. IEEE transactions on cybernetics, 48(10), 2783-2794. https://doi.org/10.1109/TCYB.2017.2750221
Cheng Tan, et al. "Delay-Dependent Algebraic Riccati Equation to Stabilization of Networked Control Systems: Continuous-Time Case." IEEE transactions on cybernetics vol. 48,10 (2018): 2783-2794. doi: https://doi.org/10.1109/TCYB.2017.2750221
Cheng Tan, Huanshui Zhang, Wing Shing Wong. Delay-Dependent Algebraic Riccati Equation to Stabilization of Networked Control Systems: Continuous-Time Case. IEEE Trans Cybern. 2018 Oct;48(10):2783-2794. doi: 10.1109/TCYB.2017.2750221. Epub 2017 Sep 19. PMID: 28945606.
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Local-Density-Based Optimal Granulation and Manifold Information Granule Description.

IEEE transactions on cybernetics

Ji Xu, Guoyin Wang, Tianrui Li, Pedrycz W.
PMID: 28945607
IEEE Trans Cybern. 2018 Oct;48(10):2795-2808. doi: 10.1109/TCYB.2017.2750481. Epub 2017 Sep 19.

Constructing information granules (IGs) has been of significant interest to the discipline of granular computing. The principle of justifiable granularity has been proposed to guide the design of IGs, opening an avenue of pursuits of building IGs carried out...

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Ji Xu, Guoyin Wang, Tianrui Li, et al. Local-Density-Based Optimal Granulation and Manifold Information Granule Description. IEEE Trans Cybern. 2017;48(10):2795-2808doi: 10.1109/TCYB.2017.2750481.
Ji Xu, Guoyin Wang, Tianrui Li, & Pedrycz, W. (2018). Local-Density-Based Optimal Granulation and Manifold Information Granule Description. IEEE transactions on cybernetics, 48(10), 2795-2808. https://doi.org/10.1109/TCYB.2017.2750481
Ji Xu, et al. "Local-Density-Based Optimal Granulation and Manifold Information Granule Description." IEEE transactions on cybernetics vol. 48,10 (2018): 2795-2808. doi: https://doi.org/10.1109/TCYB.2017.2750481
Ji Xu, Guoyin Wang, Tianrui Li, Pedrycz W. Local-Density-Based Optimal Granulation and Manifold Information Granule Description. IEEE Trans Cybern. 2018 Oct;48(10):2795-2808. doi: 10.1109/TCYB.2017.2750481. Epub 2017 Sep 19. PMID: 28945607.
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FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data.

IEEE transactions on cybernetics

Mandal A, Maji P.
PMID: 28391216
IEEE Trans Cybern. 2018 Apr;48(4):1229-1241. doi: 10.1109/TCYB.2017.2685625. Epub 2017 Apr 04.

One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously...

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Mandal A, Maji P. FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data. IEEE Trans Cybern. 2017;48(4):1229-1241doi: 10.1109/TCYB.2017.2685625.
Mandal, A., & Maji, P. (2018). FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data. IEEE transactions on cybernetics, 48(4), 1229-1241. https://doi.org/10.1109/TCYB.2017.2685625
Mandal, Ankita, and Maji, Pradipta. "FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data." IEEE transactions on cybernetics vol. 48,4 (2018): 1229-1241. doi: https://doi.org/10.1109/TCYB.2017.2685625
Mandal A, Maji P. FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data. IEEE Trans Cybern. 2018 Apr;48(4):1229-1241. doi: 10.1109/TCYB.2017.2685625. Epub 2017 Apr 04. PMID: 28391216.
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Showing 1 to 12 of 3893 entries