تنظیم بهینه و همزمان ساختار و پارامترهای شبکه عصبی با استفاده از الگوریتم آمیختار مبتنی بر جستجوی گرانشی برای کاربردهای دسته‌بندی و تقریب توابع

Determining the optimum number of nodes, number of hidden layers, and synaptic connection weights in an artificial neural network (ANN) plays an important role in the performance of this soft computing model. Several methods have been proposed for weights update (training) and structure selection of the ANNs. For example, the error back-propagation (EBP) is a traditional method for weights update of multi-layer networks. In this study, gravitational search algorithm (GSA), as a modern swarm intelligence optimization method, is used for this purpose. In this way, GSA and its binary version (BGSA) are used concurrently, as a hybrid method, to optimize the weights and number of hidden-layer nodes of an ANN, respectively. The performance of proposed method is compared with other intelligent and traditional methods such as particle swarm optimization (PSO), PSO-BPSO hybrid, and EBP in classification and function approximation tasks. The performance is evaluated over Iris, Breast Cancer, and Glass datasets for classification. For function approximation task, the performance is evaluated for a prosody predictor that is used for natural speech synthesis. To reduce the number of inputs to prosody predictor, a hybrid of an genetic algorithm and ant colony optimization algorithm is used for feature selection.  Simulation results show that the proposed method can offer competitive classification and prediction accuracies while using a reduced number of hidden neurons (25-68 percent reduction) as compared to other investigated algorithms.