William J. B. Oldham

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Concerns the problem of finding weights for feed-forward networks in which threshold functions replace the more common logistic node output function. The advantage of such weights is that the complexity of the hardware implementation of such networks is greatly reduced. If the task to be learned does not change over time, it may be sufficient to find the(More)
Dynamic task allocation for distributed computing systems (DCS) is an important goal to be achieved for engineering applications. The object is to maintain maximum utilization of computing resources while ensuring job performance at the highest productive level. The process of dynamic task allocation is to allocate or reallocate jobs (or tasks) to(More)
In this study, we have used the principle of competitive learning to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov random fields (MRFs), the image recovery problem is transformed to the problem of minimization of an energy function; A local update rule for each pixel point is then developed in a stepwise(More)
This paper explores the application of neural networks, specificallyback propagation networks, to the problem of predicting the acid concentration of WasteWater. Experiments were conducted to determine the effects of varyingnetwork parameters such as the size of the tag, the normalization technique, and the number of steps forward in time the network is(More)
The image restoration methods based on the Bayesian’s framework and Markov random fields (MRF) have been widely used in the image-processing field. The basic idea of all these methods is to use calculus of variation and mathematical statistics to average or estimate a pixel value by the values of its neighbors. After applying this averaging process to the(More)
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