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Least-Squares Design of FIR Filters Based on a Compacted Feedback Neural Network

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2 Author(s)
Yue-Dar Jou ; Dept. of Electr. Eng., Mil. Acad., Kaohsiung ; Fu-Kun Chen

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 parameter of the Hopfield neural network (HNN), the proposed approach can achieve a notable reduction both in the amount of computation required and hardware complexity compared to the previous neural-based method. Simulation results indicate the effectiveness of the proposed approach

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IEEE Transactions on Circuits and Systems II: Express Briefs  (Volume:54 ,  Issue: 5 )