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Full-Text Articles in Mechanical Engineering
Frequency Domain Robustness Analysis Of Hopfield And Modified Hopfield Neural Networks, Jie Shen, S. N. Balakrishnan
Frequency Domain Robustness Analysis Of Hopfield And Modified Hopfield Neural Networks, Jie Shen, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A variant of Hopfield neural network, called the modified Hopfield network, is formulated in this study. This class of networks consists of parallel recurrent networks which have variable dimensions that can be changed to fit the problem under consideration. It has a structure to implement an inverse transformation that is essential for embedding optimal control gain sequences. Equilibrium solutions of this network are discussed. The robustness of this network and the classical Hopfield network are carried out in the frequency domain using describing functions
Online Identification And Control Of Aerospace Vehicles Using Recurrent Networks, Zhenning Hu, S. N. Balakrishnan
Online Identification And Control Of Aerospace Vehicles Using Recurrent Networks, Zhenning Hu, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Methods for estimating the aerospace system parameters and controlling them through two neural networks are presented in this study. We equate the energy function of Hopfield neural network to integral square of errors in the system dynamics and extract the parameters of a system. Parameter convergence is proved. For control, we equate the equilibrium status of a "modified" Hopfield neural network to the steady state Riccati solution with the system parameters as inputs. Through these two networks, we present the online identification and control of an aircraft using its nonlinear dynamics.