BP neural network as a kind of intelligent method is widely used in fault diagnosis, due to the single BP neural network’s error is big, GA algorithm is often used in optimizing BP neural network, but the standard GA algorithm’s searching efficiency is low and it is easy to fall into local convergence. According to the characters of Accord car ignition diagnosis and BP neural network, this arti...

Future services demand high data rate and quality. Thus, it is necessary to define new and robust algorithms to equalize channels and reduce noise in communications. Now a days, new equalization algorithms are being developed to optimize the channel bandwidth and reduce noise, namely, Blind Channel Equalization. Conventional equalizations minimizing mean-square error generally require a trainin...

Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel estimation. It is well known that the convergence speed of ISS-LMS is fixed by the initial step-size. In the channel estimation scenarios, it is very hard to make tradeoff between convergenc...

The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the LMS algorithm. We present in this paper an multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the stan...

In this paper, we propose improved versions of normalized least mean square (NLMS) algorithm: single and multiple -variable step size normalized least mean square (VSSNLMS) algorithms for echo cancellation. The presented algorithms exhibit faster convergence rate in comparison to NLMS algorithm. Simulation results employing standard figure of merits show how the algorithms perform better than N...

We consider the convergence analysis of the sign algorithm for adaptive filtering when the input processes are uncorrelated and Gaussian. Asymptotic time-averaged convergence results for the mean deviation error, mean-square deviation error, and for the signal estimation error are established. These results are shown to hold for arbitrary step size μ > 0.

In this paper Adaptive filter is designed and simulated using different algorithms for noise reduction in different signals. The developed filter has been analyzed using Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms for sinusoidal, chirp and saw-tooth signals. The performance of developed filter has been compared interms of Rate of Con...

Designing a Least Mean Square (LMS) family adaptive algorithm includes solving the wellknown trade-off between the initial convergence speed and the mean-square error in steady state according to the requirements of the application at hands. The trade-off is controlled by the step-size parameter of the algorithm. Large step size leads to a fast initial conver‐ gence but the algorithm also exhib...