Design and Analysis of A General Recurrent Neural Network Model for Time-Varying Matrix Inversion  

Oleh:

Zhang, Yunong ; Ge, S. S.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 6 (Nov. 2005), page 1477-1490.
Topik: time; design; neural network; time - varyung; matrix inversion

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Following the idea of using first - order time derivatives, this paper presents a general recurrent neural network (RNN) model for online inversion of time - varying matrices. Different kinds of activation functions are investigated to guarantee the global exponential convergence of the neural model to the exact inverse of a given time - varying matrix. The robustness of the proposed neural model is also studied with respect to different activation functions and various implementation errors. Simulation results, including the application to kinematic control of redundant manipulators, substantiate the theoretical analysis and demonstrate the efficacy of the neural model on time - varying matrix inversion, especially when using a power - sigmoid activation function.

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