Learning in the feed-forward random neural network: A critical review
Abbreviated Journal Title
Random neural network; Learning; Gradient descent; Multi-layer; perceptron; Error functions; Evolutionary neural networks; ART; SVM; CART; Multi-objective optimization; PARTICLE SWARM OPTIMIZATION; MULTIPLE DATA SETS; VIDEO QUALITY; FUZZY-ARTMAP; SYNCHRONIZED INTERACTIONS; STATISTICAL COMPARISONS; MULTILAYER PERCEPTRONS; GLOBAL OPTIMIZATION; PATTERN-RECOGNITION; QUEUING-NETWORKS; Computer Science, Hardware & Architecture; Computer Science, Theory &; Methods
The Random Neural Network (RNN) has received, since its inception in 1989, considerable attention and has been successfully used in a number of applications. In this critical review paper we focus on the feed-forward RNN model and its ability to solve classification problems. In particular, we paid special attention to the RNN literature related with learning algorithms that discover the RNN interconnection weights, suggested other potential algorithms that can be used to find the RNN interconnection weights, and compared the RNN model with other neural-network based and non-neural network based classifier models. In review, the extensive literature review and experimentation with the RNN feed-forward model provided us with the necessary guidance to introduce six critical review comments that identify some gaps in the RNN's related literature and suggest directions for future research. (C) 2010 Elsevier B.V. All rights reserved.
"Learning in the feed-forward random neural network: A critical review" (2011). Faculty Bibliography 2010s. 1316.