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Recently, spiking neural networks (SNNs) have been shown capable of approximating the dynamics of biological neuronal networks, and of being trainable by biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity. Numerical simulations also support the possibility that they may possess universal function approximation abilities. However the effectiveness of training...
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...
A constrained approximate dynamic programming (ADP) approach is presented for designing adaptive neural network (NN) controllers with closed-loop stability and performance guarantees. Prior knowledge of the linearized equations of motion is used to guarantee that the closed-loop system meets performance and stability objectives when the plant operates in a linear parameter-varying (LPV) regime. In...
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