Robust Initialization of a Jordan Network With Recurrent Constrained Learning ABSTRACT:In this paper, we propose a robust initialization of a Jordan network with a recurrent constrained learning (RIJNRCL) algorithm for multilayered recurrent neural networks (RNNs). This novel algorithm is based on the constrained learning concept of the Jordan network with a recurrent sensitivity and weight convergence analysis, which is used to obtain a tradeoff between the training and testing errors. In addition to using classical techniques for the adaptive learning rate and the adaptive dead zone, RIJNRCL employs a recurrent constrained parameter matrix to switch off excessive contributions from the hidden layer neurons based on weight convergence and stability conditions of the multilayered RNNs. It is well known that a good response from the hidden layer neurons and proper initialization play a dominant role in avoiding local minima in multilayered RNNs. The new RIJNRCL algorithm solves the twin problems of weight initialization and selection of the hidden layer neurons via a novel recurrent sensitivity ratio analysis. We provide the detailed steps for using RIJNRCL in a few benchmark time-series prediction problems and show that the proposed algorithm achieves superior generalization performance. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Reduced-Size Kernel Models for Nonlinear Hybrid System Identification Sign Prediction and Volatility Dynamics With Hybrid Neurofuzzy Approaches