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The advent of silicon based integrated circuit technology and consequent growth in computer technology in the 1960s was instrumental in the general surge in artificial neural computer systems. The ADALINE introduced by Widrow and Hoff was similar to the Perceptron but it used a much better learning algorithm, called the LMS algorithm, which can also be used for adaptive filters. The extension of the LMS algorithm is used in today’s MLP. As the 1960s drew to a close there was growing optimism for the advance of ANN technology.
5. Adaptive beamforming. Adaptive equalisation of telephone channels to minimise data transmission intersymbol interference was first developed by Lucky in 1965 (Lucky 1965). He used his minimax criterion based zero-forcing algorithm to automatically adjust the tap weights of a transversal equaliser by minimising what he called the peak distortion. This pioneering work by Lucky spearheaded many other significant contributions to the adaptive equalisation problem. In 1969, Gerosho and Proakis, and Miller independently reformulated the adaptive equaliser problem using a mean square-error criterion.
I[k] = the innovation vector at instance k. s[k] = the state at instance k. 4 contains the new information (being the observed new data at time k less its linear prediction based on observations up to and including time k-1) that is presented to the filter at the time of the processing for the instance k. As there is a one-to-one correspondence between the Kalman and RLS variables it is possible to learn useful ideas for RLS from the vast Kalman filter literature. There are three main categories of RLS depending on the specific approach taken (Haykin 1996), 1.