A measure of satisfaction with food-related life

A measure of satisfaction with food-related life is developed and tested in three studies in eight European countries. Five items are retained from an original pool of seven; these items exhibit good reliability as measured by Cronbach's alpha, good temporal stability, convergent validity with two related measures, and construct validity as indicated by relationships with other indicators of quality of life, including the Satisfaction With Life and the SF-8 scales. It is concluded that this scale will be useful in studies trying to identify factors contributing to satisfaction with food-related life.

Empowerment, Quality of Life and Service Satisfaction: comparisons between hospitalised continuing care patients and deinstitutionalised patients

Objectives: Recent advances in mental health care policy and service delivery have lead to the development of community care initiatives which have enabled those individuals traditionally cared for in hospital environments to be resettled successfully in community living arrangements that foster an ethos of empowerment and recovery. This study sought to identify differences between a hospital continuing care group (n = 16) and a community placement group (n = 20) in relation to quality of life, satisfaction and levels of empowerment. Method: The study was a cross-sectional design. It follows up a cohort of individuals identified as the ‘hospital continuing care group’ (365+ consecutive days in psychiatric hospital care) by Homefirst Community Trust in Northern Ireland. A proportion of this population has been resettled into community care environments and some continue to reside in hospital. Patients both in the hospital continuing care group and the community placement group completed two standard questionnaires that covered a number of variables including empowerment, quality of life and service satisfaction. Results: There were significant differences between the hospital continuing care and community placement groups across scores on service satisfaction, quality of life, and empowerment in the current study. Hypotheses relating to service satisfaction (z = -4.117; p

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.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.