A recursive rule base adjustment algorithm for a fuzzy logic controller

This paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers. Here the fuzzy controller is constructed on the basis of a decision table (DT), relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience. If the controller performance is not satisfactory, it has previously been suggested that the rule base be altered by combined tuning of membership functions and controller scaling factors. The alternative approach proposed here entails alteration of the fuzzy rule base. The recursive rule base adjustment algorithm proposed in this paper has the benefit that it is computationally more efficient for the generation of a DT, and advantage for online realization. Simulation results are presented to support this thesis. (c) 2005 Elsevier B.V. All rights reserved.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Potentially Inappropriate Medications Defined by STOPP Criteria and the Risk of Adverse Drug Events in Older Hospitalized Patients

Background: Previous studies have not demonstrated a consistent association between potentially inappropriate medicines (PIMs) in older patients as defined by Beers criteria and avoidable adverse drug events (ADEs). This study aimed to assess whether PIMs defined by new STOPP (Screening Tool of Older Persons’ potentially inappropriate Prescriptions) criteria are significantly associated with ADEs in older people with acute illness.

Methods: We prospectively studied 600 consecutive patients 65 years or older who were admitted with acute illness to a university teaching hospital over a 4-month interval. Potentially inappropriate medicines were defined by both Beers and STOPP criteria. Adverse drug events were defined by World Health Organization–Uppsala Monitoring Centre criteria and verified by a local expert consensus panel, which also assessed whether ADEs were causal or contributory to current hospitalization. Hallas criteria defined ADE avoidability.Wecompared the proportions of patients taking Beers criteria PIMs
and STOPP criteria PIMs with avoidable ADEs that were causal or contributory to admission.

Results: A total of 329 ADEs were detected in 158 of 600 patients (26.3%); 219 of 329 ADEs (66.6%) were considered causal or contributory to admission. Of the 219 ADEs, 151(68.9%)considered causal or contributory to admission were avoidable or potentially avoidable. After adjusting for age, sex, comorbidity, dementia, baseline activities of daily living function, and number of medications, the likelihood of a serious avoidable ADE increased significantly when STOPP PIMs were prescribed (odds ratio, 1.847; 95% confidence interval [CI], 1.506-2.264; P.001); prescription of Beers criteria PIMs did not significantly increase ADE risk (odds ratio, 1.276; 95% CI, 0.945-1.722; P=.11).

Conclusion: STOPP criteria PIMs,unlike Beers criteria PIMs, are significantly associated with avoidable ADEs in older people that cause or contribute to urgent hospitalization.