Optimal control: linear quadratic methods
Optimal control: linear quadratic methods
Systems & Control Letters
A Novel Approach Solving for Linear Matrix Inequalities UsingNeural Networks
Neural Processing Letters
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Constrained multi-variable generalized predictive control using a dual neural network
Neural Computing and Applications
A linear matrix inequality approach to robust H∞filtering
IEEE Transactions on Signal Processing
A recurrent neural network for real-time semidefinite programming
IEEE Transactions on Neural Networks
A neural network for linear matrix inequality problems
IEEE Transactions on Neural Networks
Design and analysis of a general recurrent neural network model for time-varying matrix inversion
IEEE Transactions on Neural Networks
A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems
IEEE Transactions on Neural Networks
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A simplified neural network model is proposed to solve a class of linear matrix inequality problems. The stability and solvability of the proposed neural network are analyzed and discussed theoretically. In comparison with the previous neural network models (Lin and Huang, Neural Process Lett 11:153---169, 2000; Lin et al., IEEE Trans Neural Netw 11:1078---1092, 2000), the simplified one is composed of two layers rather than three layers, and the neuron array in each layer is triangular rather than square. The proposed approach can therefore reduce the complexity of the neural network architecture. In addition, the simplified neural network can also be extended to solve multiple linear matrix inequalities with specific constraints, which enlarges the application domain of the proposed approach. Finally, examples are given to illustrate the effectiveness and efficiency of the simplified neural network.