In this paper, we use an artificial neural network (ANN) to design a compact microstrip diplexer with wide fractional
bandwidths (FBW) for wideband applications. For this purpose, a multilayer perceptron neural network model trained with
the back-propagation algorithm is used. First, a novel resonator consists of coupled lines loaded by similar patch cells is
proposed. Then, using the proposed ANN model, two mathematical equations for S11 and S21 are obtained to achieve the
best configuration of the proposed bandpass filters and tune their resonant frequencies. Finally, using the obtained bandpass filters, a high-performance microstrip diplexer is created. The first channel of the diplexer is from 1.47 GHz up to
1.74 GHz with a wide FBW of 16.8%. The second channel is expanded from 2 to 2.23 GHz with a fractional bandwidth of
11%. In comparison with the previous designs, our diplexer has the most compact size. Moreover, the insertion losses at
both channels are improved so that they are 0.1 dB and 0.16 dB at the lower and upper channels, respectively. Both
channels are flat with a maximum group delay of 2.6 ns, which makes it suitable for high data rate communication links.
To validate the designing method and simulation results, the presented diplexer is fabricated and measured.