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Adaptive equalisation in digital communication systems is a process of compensating the disruptive effects caused mainly by inter symbol interference in a band-limited channel and plays a vital role for enabling higher data rate in modern digital communication system. Designing efficient equalisers having low structural complexity and faster learning algorithms is also an area of much research interest in the present scenario. This research work proposes adaptive channel equalisation techniques on Recurrent Neural Network framework. Exhaustive simulation studies carried out prove that by replacing the conventional sigmoid activation functions in each of the processing nodes of recurrent neural network with multilevel sigmoid activation functions, the bit error rate performance have significantly improved. Further slopes of different levels of the multi-level sigmoid have been adapted using fuzzy logic control concept Simulation results cosidering standard channel models show faster learning with less number of training samples and performance level comparable to the their conventional counterparts. Also there is scope for parallel implementation of slope adaptation technique in real-time implementation.