Evolutionary Dendritic Neural Model for Classification Problems

Abstract

In this paper, an evolutionary dendritic neuron model (EDNM) is proposed to solve classification problems. It utilizes synapses and dendritic branches to implement the nonlinear computation. Distinct from the classical dendritic neuron model (CDNM) trained by the backpropagation (BP) algorithm, the proposed EDNM is trained by a metaheuristic cuckoo search (CS) algorithm instead, which has been regarded as a global searching algorithm. CS algorithm enables EDNM to avoid several disadvantages, such as slow convergence, trapping into local minimum, and being sensitive to initial values. To evaluate the performance of EDNM, we compare it with a multilayer perceptron (MLP) and CDNM on two benchmark classification problems. The experimental results demonstrate that EDNM is superior to MLP and CDNM in terms of accuracy rate, receiver operator characteristic curve (ROC), and convergence speed. In addition, the neural structure of EDNM can be replaced by a logical circuit completely, which can be implemented in hardware easily. The corresponding experimental results also verify the effectiveness of the logical circuit classifier.