A small 4-channel time-delayed complex perceptron is used as a silicon photonic neural network (PNN) device to compensate for chromatic dispersion in optical fiber links. The PNN device is experimentally tested with non-return-to-zero optical signals at 10 Gbps after propagation through up to 125 km optical fiber link. During the learning phase, a separation-loss function is optimized in order to maximally separate the transmitted levels of 0s from the 1s, which implies an optimization of the bit-error-rate. Testing of the PNN device shows that the excess losses introduced by the PNN device are compensated by the gain in the transmitted signal equalization for a link longer than 100 km. The measured data are reproduced by a model that accounts for the optical link and the PNN device. This allows simulating the network performances for higher data rates, where the device shows improvement with respect to the benchmark both in terms of performance and ease of use.