Improved Adaptive Neural Fuzzy Filter And Its Application in Noise Cancellation

Abstract

A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. Then adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In this new ANFF, we also made the learning and fuzziness parameters adaptive. In parameter learning phase, a back propagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. Two major advantages of the ANFF can thus be seen: 1) a prior knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given since the ANFF can find its optimal structure and parameters automatically. To demonstrate the performance of this new ANFF, an application, adaptive noise cancellation, is simulated. Efficiency and advantages of new ANFF are verified by these simulations and comparisons.