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DESIGN OF MULTI-LAYER NEURAL NETWORKS FOR BUTTERWORTH FILTER OPTIMIZATION | ||
| Al-Qadisiyah Journal for Engineering Sciences | ||
| Article 1, Volume 2, Issue 1, 2009, Pages 58-65 PDF (0 K) | ||
| Author | ||
| Hanan A. R. Akkar | ||
| Abstract | ||
| In this paper a proposed design of five multi-layer feed-forward Artificial Neural Networks (ANNs) is presented for optimized Butterworth filter. The first and second network perform Butterworth ideal Low Pass Filter (LPF) and typical LPF. The third ANN performs Band Pass Filter (BPF). The fourth network perform multi–BPF which consists of two layers, the first layer consists of six tansig neurons and the second layer consists of one purline neuron, and the fifth feed-forward network is designed to perform the High Pass Filter (HPF) which consists of three layers, the first layer consists of three tansig neurons, the second layer consists of three tansig neurons and the third layer consists of one purline neuron. Back-propagation training algorithm is used to train the proposed networks with Mean Square Error (MSE) equals 10-10. Simulation and test programs are implemented by using MATLAB | ||
| Keywords | ||
| Artificial Neural Networks; Digital Signal Processing; Filters | ||
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