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Multiple-input-multiple-output (MIMO) wireless technology is a viable option likely to be able to meet the demands of the ever-expanding mobile networks. For MIMO system, channel estimation is still a challenging area due to several difficulties. Soft-computational approaches can be additions to the list of traditional methods of MIMO channel modelling primarily because these tools, for their ability to learn, are better placed to use channel side information for improved performance. One of the viable means of such innovative channel estimation is the use of the artificial neural network (ANN) in a feedforward format known as multi-layer perceptron (MLP). But as these ANNs prove to be suitable for static and slowly varying cases, time-varying MIMO channels are modelled using modified MLP with temporal attributes developed using finite impulse response (FIR) and infinite impulse response (IIR) blocks in place of the synaptic links. Six sub-classes of each of the FIR-MLP and the IIR-MLP are formulated, which show better performance than the conventional MLP in modelling the MIMO channels.