Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications

A common problem in Data Mining (DM) is the presence of noise in the data being mined. Artificial neural networks (ANN) are robust and have a good tolerance to noise, which makes them suitable for mining very noisy data. Although they may achieve high classification accuracy, they have the well-known disadvantage of having black-box nature and not discovering any high-level rule that can be used as a support for human understanding. The main challenge in using ANN in DM applications is to get explicit knowledge from these models. For this purpose, a study on knowledge acquirement from trained ANNs for classification problems is presented. The proposed method uses Touring Ant Colony Optimization (TACO) algorithm for extracting accurate and comprehensible rules from databases via trained artificial neural networks. The suggested algorithm is experimentally evaluated on different benchmark data sets. Results show that the proposed approach has a potential to generate accurate and concise rules

Global optimal design of IIR filters via constraint transcription and filled function methods

In this paper, we consider a globally optimal design of IIR filters. We formulate the design problem as a nonconvex optimization problem with a continuous inequality constraint and a nonconvex constraint. To solve this problem, the constraint transcription method is applied to tackle the continuous inequality constraint. In order to avoid the obtained solution being on the boundary of the feasible set, more than one initial points are used. Moreover, since the objective and the constraints are nonconvex functions, there may be many local minima. To address this problem, the filled function method is applied to escape from the local minima. Some numerical computer simulation results are presented to illustrate the effectiveness and efficiency of the proposed method