Differential evolution training algorithm for dendrite morphological neural networks

Dendrite morphological neural networks are emerging as an attractive alternative for pattern classification, providing competitive results with other classification methods. A key problem in the design of these neural networks is the election of the number of their dendrites. Most training methods are heuristics that do not optimize the learning parameters. Therefore, we propose a new training algorithm for classification tasks based on an optimization approach: differential evolution. We show that the besought method increases classification performance and also optimizes the number of dendrites. For generating the initial population of hyper-boxes, we adopt two techniques: one based on the division of an initial hyper-box, and the other on an initial clustering using the so-called k-means++. Both alternatives were tested on four synthetic and 11 real databases as benchmarks overcoming the state-of-the-art morphological neuron training methods as well as the radial basis networks. The proposed training algorithm achieved a favorable average accuracy compared with the well-known multilayer perceptrons and support vector machines. In addition, a real-life problem was solved by this method to recognize geometric figures using a Nao robot.