A proposed particle swarm classifier has been integrated with the concept of intelligently controlling the search process of PSO to develop an efficient swarm intelligence based classifier, which is called intelligent particle swarm classifier (IPS-classifier). This classifier is described to find the decision hyperplanes to classify patterns of different classes in the feature space. An intelligent fuzzy controller is designed to improve the performance and efficiency of the proposed classifier by adapting three important parameters of PSO (inertia weight, cognitive parameter and social parameter). Three pattern recognition problems with different feature vector dimensions are used to demonstrate the effectiveness of the introduced classifier: Iris data classification, Wine data classification and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers.

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