This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential- equation (MDE) to a vector-differentialequation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Zhang, Y., Chen, K., Weimu, M., & Li, X. D. (2007). MATLAB simulation of gradient-based neural network for online matrix inversion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4682 LNAI, pp. 98–109). Springer Verlag. https://doi.org/10.1007/978-3-540-74205-0_12
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