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In this paper, we present some results on error detection and correction in a modular neurocomputer that are based on redundant residue number systems. The error correction method developed below involves the modified Chinese Remainder Theorem with fractions and uses a Hopfield neural network to correct the errors. The suggested approach eliminates the need for extending the bases of a residue number system, a costly operation required in case of syndrome decoding with error syndromes calculation on the control bases of the system. Also the approach does not utilize the projection method, another costly operation intended to localize errors (i.e., to detect the moduli associated with faulty digits). The well-known procedures mentioned above seem inefficient in terms of practical implementation, as they employ a mixed radix number system: transition to such a system is iterative and may affect the performance of a whole neurocomputer. Owing to the exclusion of these costly operations, the suggested approach significantly simplifies error correction procedures for integer numbers. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced M-ary SVM algorithm in combination with error correction coding method is presented for multi-category classification. The main process of the approach is divided into three steps. It is firstly to generate a group of best codes based on information codes derived from the original category flags information. Secondly, it is to utilize such codes as the basis for training the classifier. The third step is the final feed-forward phase. The output codes composed of each sub-classifier are corrected by error detection and correction principle whenever any identifying error occurs. The improved algorithm not only maintains the highly simplified architecture of the standard M-ary algorithm, but also improves its generalization ability. The experimental results confirm s the effectiveness of the improved algorithm brought about by introducing as few sub-classifiers. Finally, an application example for shuttle kiln control system is shown. [ABSTRACT FROM AUTHOR]