Off-campus UMass Amherst users: To download dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users, please click the view more button below to purchase a copy of this dissertation from Proquest.
(Some titles may also be available free of charge in our Open Access Dissertation Collection, so please check there first.)
New digital structure designs of neural networks and filter banks
Abstract
The rapid development of modern industry and technology has dramatically increased the demands on the signal information system. The performance of a signal system is measured by its accuracy, speed and cost for a signal process. In implementation of a digital system such as a digital computer or a micro-processor, the basic computational operations are multiplication, addition and delay. It is well known that, among these operations, multiplication is the slowest and the most complex. Thus, the existence of multipliers in hardware implementation and multiplications in software coding are often the bottleneck of an efficient design. Therefore, it is desired to implement a signal system which is multiplier-free or multiplier-minimized. This dissertation studies some structure design for the Multi-layer Neural Network (MNN) and Cellular Neural Network (CNN). The designs are based on the Differential Digital Analyzer (DDA) technique, the CORDIC algorithm and the Convergence Computation Method (CCM). These designs have the desired multiplier-free feature and low complexity and are suitable for VLSI implementation. Efficient design of the M-channel QMF bank has also been investigated and proposed in the dissertation. The design proposed is based on the Interpolated FIR design approach, in conjunction with a cosine-modulated QMF bank system. Compared with conventional design approach, our new design reduces the number of multiplications for the filter bank and can achieve more than 50% saving of computation depending on the choice of the interpolation rate and this computational saving becomes more significant when the number of channels in a filter bank is large.
Subject Area
Electrical engineering|Computer science|Artificial intelligence
Recommended Citation
Liu, Xiaozhou, "New digital structure designs of neural networks and filter banks" (1996). Doctoral Dissertations Available from Proquest. AAI9619409.
https://scholarworks.umass.edu/dissertations/AAI9619409