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Competition Aided with Finite-Time Neural Network

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Competition-Based Neural Networks with Robotic Applications

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

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Abstract

In this chapter, a class of recurrent neural networks to solve quadratic programming problems are presented and further extended to competition generation. Different from most existing recurrent neural networks for solving quadratic programming problems, the proposed neural network model converges in finite time and the activation function is not required to be a hard-limiting function for finite convergence time. The stability, finite-time convergence property and the optimality of the proposed neural network for solving the original quadratic programming problem are proven in theory. Extensive simulations are performed to evaluate the performance of the neural network with different parameters. In addition, the proposed neural network is applied to solving the k-winner-take-all (k-WTA) problem. Both theoretical analysis and numerical simulations validate the effectiveness of our method for solving the k-WTA problem.

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Authors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, China

    Shuai Li

  2. Lanzhou University, Lanzhou, China

    Long Jin

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  1. Shuai Li
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  2. Long Jin
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Correspondence to Shuai Li .

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Li, S., Jin, L. (2018). Competition Aided with Finite-Time Neural Network. In: Competition-Based Neural Networks with Robotic Applications. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-4947-7_3

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  • DOI: https://doi.org/10.1007/978-981-10-4947-7_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4946-0

  • Online ISBN: 978-981-10-4947-7

  • eBook Packages: EngineeringEngineering (R0)

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