Artificial immune system training algorithm for a dendritic neuron model

Dendritic neuron model (DNM), which is a single neuron model with a plastic structure, has been applied to resolve various complicated problems. However, its main learning algorithm, namely the back-propagation (BP) algorithm, suffers from several shortages, such as slow convergence rate, being easy to fall into local minimum and over-fitting problems. That largely limits the performances of the DNM. To address this issue, another bio-inspired learning paradigm, namely the artificial immune system (AIS) is employed to train the weights and thresholds of the DNM, which is termed AISDNM. These two methods have advantages on different issues. Due to the powerful global search capability of the AIS, it is considered to be efficient in improving the performance of the DNM. To evaluate the performance of AISDNM, eight classification datasets and eight prediction problems are adopted in our experiments. The experimental results and statistical analysis confirm that the AISDNM can exhibit superior performance in terms of accuracy and convergence speed when compared with the multilayer perceptron (MLP), decision tree (DT), the support vector machine with the linear kernel (SVM-l), the support vector machine with the radial basis function kernel (SVM-r), the support vector machine with the polynomial kernel (SVM-p) and the conventional DNM. It can be concluded that the reasonable combination of two different bio-inspired learning paradigms is efficient. Furthermore, for the classification problems, empirical evidence also validates the AISDNM can delete superfluous synapses and dendrites to simplify its neural structure, then transform the simplified structure into a logic circuit classifier (LCC) which is suitable for hardware implementation. The process does not sacrifice accuracy but significantly improves the classification speed. Based on these results, both the AISDNM and the LCC can be regarded as effective machine learning techniques to solve practical problems.