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Comparison and evaluation of two neural network models used to classify transmitting devices
In recent times, artificial neural networks have shown excellent performance in the area of pattern recognition as an alternative to conventional statistical pattern recognition. The neural network approach assumes no ...
Fuzzy neural networks
(Texas Tech University, 1998-12)
Since the development of computer technology, methods have been developed and investigated to mimic the processes of the human brain. The human brain is a collection of billions of neurons interconnected with each other. ...
Digital VLSI implementation of artificial neural network systems
(Texas Tech University, 1993-05)
Using sigmoidal nodes to train hard-limiter networks
It is often desirable to have neural networks whose output is hard-limited, since these networks are easy to implement in hardware and provide simplistic output. However, the methods for training hard-limiter neural ...
Recurrent neural networks and time series prediction
This work presents an exploration of the dynamic behavior of small recurrent networks. Proofs are presented to show that fixed points and hmit cycles can be produced by small recurrent networks. As an apphcation. a network ...
Quantitative analyses of associative memories
(Texas Tech University, 1992-05)
Neural networks have been studied for many years in the hope of simulating human-like activities such as recognizing a friend in a picture. Associative memories are systems that can recall stored data by specifying all or ...
A perceptron-based optical quadratic neural network using a photorefractive crystal
An optical quadratic neural network (OQNN) utilizing four-wave mixing in barium titanate (BaTi03 ) has been investigated and developed. This network implements a feedback loop using a charged-coupled device (CCD) camera, ...
An on-chip learning neural network
In this thesis, an on-chip learning neural network has been designed that uses multiplying digital-to-analog-converters (MDAC) in the synapse. The chip will take advantage of digital processing to learn and store weights, ...
Recurrent neural networks and the Pearlmutter algorithm
(Texas Tech University, 1996-05)