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A constrained penalty function method for exploratory adaptive-critic neural network (NN) control is presented. While constrained approximate dynamic programming has been effective to guarantee closed-loop system performance and stability objectives, in the presence of a change in the plant dynamics it may not have the necessary plasticity to explore and fully adapt to the new behaviors of the plant,...
A constrained-backpropagation (CPROP) training technique is presented to solve partial differential equations (PDEs). The technique is based on constrained optimization and minimizes an error function subject to a set of equality constraints, provided by the boundary conditions of the differential problem. As a result, sigmoidal neural networks can be trained to approximate the solution of PDEs avoiding...
Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximators. Spiking neural networks offer several advantages over sigmoidal networks, because they can approximate the dynamics of biological neuronal networks, and can potentially reproduce the computational speed observed in biological...
A constrained-backpropagation training technique is presented to suppress interference and preserve prior knowledge in sigmoidal neural networks, while new information is learned incrementally. The technique is based on constrained optimization, and minimizes an error function subject to a set of equality constraints derived via an algebraic training approach. As a result, sigmoidal neural networks...
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