Abstract
Fuzzy inference systems are used in many control problems, but this approach is also effective in decision making and signal analysis. Decision making has often to deal with vagueness, associated with natural phenomena, where incomplete knowledge and hardly understood mechanisms, hinder straightforward conclusion making. In the context of analyzing time series of various types of data, a fuzzy inference system can be used to recognize patterns in the data. A novelty in this approach is the use of Controlled Random Search, a zero-order optimization method, to tune membership function shapes. The technique has been tested by implementing a fuzzy inference system for estrus detection. Using routinely collected time series the system detects cows in standing heat with acceptable results.
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