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This paper extends the neural network based algorithm for equiripple design of higher-order digital differentiators in the weighted least-squares sense. The proposed approach formulates an error representation reflecting the difference between the desired amplitude response and the designed response in a Lyapunov error function. The optimal filter coefficients are obtained when neural network achieves...
An improved neural-based approach for the design of FIR all-pass phase equalizer with prescribed magnitude and phase responses is introduced. The error differences in the frequency domain are formulated as a Lyapunov energy function. By mapping the objection function to the corresponding Hopfield neural network, the optimal filter coefficients are therefore obtained using a parallel manner. Simulation...
The design of finite-impulse response (FIR) filters can be performed by using neural networks by formulating the objective function to a Lyapunov energy function. Focusing on this goal, the authors present an improved structure of a feedback neural network to implement the least-squares design of FIR filters. In addition to using the closed-form expressions for the synaptic weight matrix and the bias...
In this paper, a neural network based approach is extended to the design of arbitrary complex FIR filters. The minimization of difference between the frequency responses of desired FIR filter and the designed one is formulated as Lyapunov energy function. The filter coefficients can be therefore obtained by using a set of dynamic non-linear equations. As compared to linear algebra based approaches,...
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