A methodology for the automated development of fuzzy expert systems is presented. The idea is to start with a crisp model described by crisp rules and then transform them into a set of fuzzy rules, thus creating a fuzzy model. The adjustment of the model's parameters is performed via a stochastic global optimization procedure. The proposed methodology is tested by applying it to problems related to cardiovascular diseases, such as automated arrhythmic beat classification and automated ischemic beat classification, which, besides being well-known benchmarks, are of particular interest due to their obvious medical diagnostic importance. For both problems, the initial set of rules was determined by expert cardiologists, and the MIT-BIH arrhythmia database and the European ST-T database are used for optimizing the fuzzy model's parameters and evaluating the fuzzy expert system. In both cases, the results indicate an escalation of the performance from the simple initial crisp model to the more sophisticated fuzzy models, proving the scientific added value of the proposed framework. Also, the ability to interpret the decisions of the created fuzzy expert systems is a major advantage compared to "black box" approaches, such as neural networks and other techniques.

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We introduce a new variant for the constriction coefficient model of the established particle swarm optimization (PSO) algorithm. The new variant stands between the synchronous and asynchronous version of PSO, combining their operation regarding the update and evaluation frequency of the particles. Yet, the proposed variant has a unique feature that distinguishes it from other approaches. Specifically, it allows the undisrupted move of all particles even though evaluating only a portion of them. Apparently, this implies a loss of information for PSO, but it also allows the full exploitation of the convergence dynamic of the constriction coefficient model. Moreover, it requires only minor modifications to the original PSO algorithm since it does not introduce complicated procedures. Experimental results on widely used benchmark problems as well as on problems drawn from real-life applications, reveal that the proposed approach is efficient and can be very competitive to other PSO variants as well as to more specialized approaches.

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