Model Predictive Control was developed in the late 70's and came into widespread use, particularly in the refining industry, in the 80's. The economic benefit of this approach to control has been documented [1,2].

Model Predictive Control was developed in the late 70's and came into widespread use, particularly in the refining industry, in the 80's. The economic benefit of this approach to control has been documented [1,2] .

Neural network algorithms have proven useful for recognition of individual, segmented characters. However, their recognition accuracy has been limited by the accuracy of the underlying segmentation algorithm. Conventional, rule-based segmentation algorithms encounter difficulty if the characters are touching, broken, or noisy. The problem in these situations is that often one cannot properly segment a character until it is recognized yet one cannot properly recognize a character until it is segmented. We present here a neural network algorithm that simultaneously segments and recognizes in an integrated system. This algorithm has several novel features: it uses a supervised learning algorithm (backpropagation), but is able to take position-independent information as targets and self-organize the activities of the units in a competitive fashion to infer the positional information. We demonstrate this ability with overlapping hand-printed numerals.

Neural network algorithms have proven useful for recognition of individual, segmentedcharacters. However, their recognition accuracy has been limited by the accuracy of the underlying segmentation algorithm. Conventional, rule-basedsegmentation algorithms encounter difficulty if the characters are touching, broken, or noisy. The problem in these situations is that often one cannot properly segment a character until it is recognized yetone cannot properly recognize a character until it is segmented. We present here a neural network algorithm that simultaneously segments and recognizes in an integrated system. This algorithm has several novel features: it uses a supervised learning algorithm (backpropagation), but is able to take position-independent information as targets and self-organize the activities of the units in a competitive fashion to infer the positional information. We demonstrate this ability with overlapping hand-printed numerals.

Data is usually available in the form of a set S consisting of {x,y} pairs, where x is a input vector and y is the corresponding output vector.

Data is usually available in the form of a set S consisting of {x,y} pairs, where x is a input vector and y is the corresponding output vector.

ABSTRACT The information capacity of Kanerva's Sparse, Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and HopJreld-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns. INTRODUCTION Many different models of memory and thought have been proposed by scientists over the years. The learning rule considered here uses the outer-product of patterns of Is and -Is.

ABSTRACT The information capacity of Kanerva's Sparse, Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total informationstored in these systems is proportional to the number connections in the network. Theproportionality constant is the same for the SDM and HopJreld-type models independent ofthe particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences ofspatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns. INTRODUCTION Many different models of memory and thought have been proposed by scientists over the years.