## 6 Other Algorithms  
Additional systems are currently being investigated using computer models for blood glucose control. H-infinity loops attempt to minimize the worst-case gain in insulin delivery by altering insulin infusion rates from a set of pre-determined basal rates based on glucose values estimated from noisy measurements. Simulation studies showed that the system could correct blood glucose deviation using clinically acceptable insulin delivery rates, with the surmised advantage of reducing insulin requirements and the effects of hyperinsulinemia [35]. However, this method employs mathematical models that are very complex, requiring expert guidance in their management.  
Fuzzy logic, on the contrary, has been used since its conception in the latter part of the last century to assist with the processing of large amounts of data by allowing 'partial set membership' rather than crisp'membership' or 'no membership' [36]. Using this graded membership for given variables, and defining interaction within and between them (i.e. fuzzification of crisp values), a logic circuit for evaluation of input is created that flows through the 'fuzzy filter'. Defuzzification provides crisp output data that determines the response of the system to input analysis, generating feedback to the system that can then be re-analyzed through the loop [37]. The advantage of fuzzy control systems lies in their ability to simplify input recognition and analysis using graded, or 'fuzzied', parameters. Multiple input values therefore add minimal complexity overall, but increase stability considerably [38], inherently creating fail-safes that prevent wide fluctuations in the system. These systems have found wide applicability in other aspects of the medical field, including electro-diagnosis, control of blood pressure during anesthesia and nutrition advice.  
Neural networks have come into play as key components of insulin-delivery control systems within the last few decades, not only as adjunctive components (e.g. assisting in model analysis) but also as the algorithm processor itself [39]. The adaptive capability of neural networks has been assessed within and without the medical literature, possessing remarkable self-learning and self-improving potential. In conjunction with other control systems, as shown in a randomized prospective trial of 40 critically ill patients with diabetes [40], this potential may be realized in the setting of glucagonidation. Hypoglycemic rates were almost half as common using a fuzzy-neural method of control compared to conventional algorithms, with significant improvement in glycemic control overall.  
In summary, it is now possible to program the thought processes of experts into a controller that regulates the output of an insulin delivery system based on frequently-acquired glucose concentration data. The more convenient route of insulin administration (subcutaneous) leads to a substantial efferent delay due to its delayed absorption and action. For this reason, glucose levels are often elevated after meals and often too low at later periods when the insulin finally exerts this effect. In subcutaneous systems, the effect of post-prandial hyperglycemia can be mitigated by giving some of the insulin in an open loop fashion before the meal. In addition, the late episodes of hypoglycemia can likely be minimized by the delivery of small doses of glucagon as glucose falls towards low levels. Given the personal and economic burden of diabetes mellitus, closed loop (or hybrid) systems hold great promise for reducing its acute and chronic complications.