Linear Bayesian neurons
J.E. Hudson · 1991
The serious use of neural networks in real-time applications is handicapped by uncertain and possibly lengthy convergence times and unknown final performance. To a large extent these difficulties are removed in the Bayesian neural nets, whose final state is that of a likelihood computer and which have fast training. Whether these are neural networks in the strict sense is debatable but they show many of the characteristics of multi-layer perceptrons. Such networks have obvious attractions in applications involving signal detection in noise. The application to demodulation of noisy data corrupted by intersymbol interference is treated. >