274-281Neural network
adaptive filters are mainly used for the interference cancellation techniques.
The gradient based design methods are well developed for the design of neural
network adaptive filter but they converge to local minima. This paper describes
the global optimization interference cancelling techniques for adaptive
filtering of interferences in the corrupted signal. The system is designed
using the adaptive filtering of the interferences in the corrupted signal using
the Back Propagation Neural Network (BPNN) algorithm, Genetic Algorithm (GA),
and Bee Colony (BC) algorithm. These optimization algorithms are used for
initialization of weights, learning parameters, activation function and
selection of network structure of the artificial neural network. The adaptive
filtering system is designed using an adaptive learning ability of BPNN
algorithm. This paper presents a comparison of evolutionary optimization
algorithm such as hybrid GA-BPNN and BC-BPNN algorithm for the interference
cancellation in corrupted signals